arcgis.learn module

Functions for calling the Deep Learning Tools.

Data Preparation Methods

export_training_data

arcgis.learn.export_training_data(input_raster, input_class_data=None, chip_format=None, tile_size=None, stride_size=None, metadata_format=None, classvalue_field=None, buffer_radius=None, output_location=None, context=None, input_mask_polygons=None, rotation_angle=0, reference_system='MAP_SPACE', process_all_raster_items=False, blacken_around_feature=False, fix_chip_size=True, additional_input_raster=None, input_instance_data=None, instance_class_value_field=None, min_polygon_overlap_ratio=0, *, gis=None, future=False, estimate=False, **kwargs)

Function is designed to generate training sample image chips from the input imagery data with labeled vector data or classified images. The output of this service tool is the data store string where the output image chips, labels and metadata files are going to be stored.

Note

This function is supported with ArcGIS Enterprise (Image Server)

Parameter	Description
input_raster	Required `ImageryLayer`/`Raster`/`Item`/String (URL). Raster layer that needs to be exported for training.
input_class_data	Labeled data, either a feature layer or image layer. Vector inputs should follow a training sample format as generated by the ArcGIS Pro Training Sample Manager. Raster inputs should follow a classified raster format as generated by the Classify Raster tool.
chip_format	Optional string. The raster format for the image chip outputs. `TIFF`: TIFF format `PNG`: PNG format `JPEG`: JPEG format `MRF`: MRF (Meta Raster Format)
tile_size	Optional dictionary. The size of the image chips. Example: {“x”: 256, “y”: 256}
stride_size	Optional dictionary. The distance to move in the X and Y when creating the next image chip. When stride is equal to the tile size, there will be no overlap. When stride is equal to half of the tile size, there will be 50% overlap. Example: {“x”: 128, “y”: 128}
metadata_format	Optional string. The format of the output metadata labels. There are 4 options for output metadata labels for the training data, KITTI Rectangles, PASCAL VOCrectangles, Classified Tiles (a class map) and RCNN_Masks. If your input training sample data is a feature class layer such as building layer or standard classification training sample file, use the KITTI or PASCAL VOC rectangle option. The output metadata is a .txt file or .xml file containing the training sample data contained in the minimum bounding rectangle. The name of the metadata file matches the input source image name. If your input training sample data is a class map, use the Classified Tiles as your output metadata format option. `KITTI_rectangles`: The metadata follows the same format as the Karlsruhe Institute of Technology and Toyota echnological Institute (KITTI) Object Detection Evaluation dataset. The KITTI dataset is a vision benchmark suite. This is the default.The label files are plain text files. All values, both numerical or strings, are separated by spaces, and each row corresponds to one object. This format can be used with FasterRCNN, RetinaNet, SingleShotDetector and YOLOv3 models. `PASCAL_VOC_rectangles`: The metadata follows the same format as the Pattern Analysis, Statistical Modeling and Computational Learning, Visual Object Classes (PASCAL_VOC) dataset. The PASCAL VOC dataset is a standardized image data set for object class recognition.The label files are XML files and contain information about image name, class value, and bounding box(es). This format can be used with FasterRCNN, RetinaNet, SingleShotDetector and YOLOv3 models. `Classified_Tiles`: This option will output one classified image chip per input image chip. No other meta data for each image chip. Only the statistics output has more information on the classes such as class names, class values, and output statistics. This format can be used with BDCNEdgeDetector, DeepLab, HEDEdgeDetector, MultiTaskRoadExtractor, PSPNetClassifier and UnetClassifier models. `RCNN_Masks`: This option will output image chips that have a mask on the areas where the sample exists. The model generates bounding boxes and segmentation masks for each instance of an object in the image. This format can be used with MaskRCNN model. `Labeled_Tiles`: This option will label each output tile with a specific class. This format is used for image classification. This format can be used with FeatureClassifier model. `MultiLabeled_Tiles`: Each output tile will be labeled with one or more classes. For example, a tile may be labeled agriculture and also cloudy. This format is used for object classification. This format can be used with FeatureClassifier model. `Export_Tiles`: The output will be image chips with no label. This format is used for image enhancement techniques such as Super Resolution and Change Detection. This format can be used with ChangeDetector, CycleGAN, Pix2Pix and SuperResolution models. `CycleGAN`: The output will be image chips with no label. This format is used for image translation technique CycleGAN, which is used to train images that do not overlap. `Imagenet`: Each output tile will be labeled with a specific class. This format is used for object classification; however, it can also be used for object tracking when the Deep Sort model type is used during training. `Panoptic_Segmentation`: The output will be one classified image chip and one instance per input image chip. The output will also have image chips that mask the areas where the sample exists; these image chips will be stored in a different folder. This format is used for both pixel classification and instance segmentation, therefore there will be two output labels folders.
classvalue_field	Optional string. Specifies the field which contains the class values. If no field is specified, the system will look for a ‘value’ or ‘classvalue’ field. If this feature does not contain a class field, the system will presume all records belong the 1 class.
buffer_radius	Optional integer. Specifies a radius for point feature classes to specify training sample area.
output_location	This is the output location for training sample data. It can be the server data store path or a shared file system path. Example: Server datastore path - `/fileShares/deeplearning/rooftoptrainingsamples` `/rasterStores/rasterstorename/rooftoptrainingsamples` File share path - `\\servername\deeplearning\rooftoptrainingsamples`
context	Optional dictionary. Context contains additional settings that affect task execution. Dictionary can contain value for following keys: exportAllTiles - Choose if the image chips with overlapped labeled data will be exported. True - Export all the image chips, including those that do not overlap labeled data. False - Export only the image chips that overlap the labelled data. This is the default. startIndex - Allows you to set the start index for the sequence of image chips. This lets you append more image chips to an existing sequence. The default value is 0. cellSize - cell size can be set using this key in context parameter extent - Sets the processing extent used by the function Setting context parameter will override the values set using arcgis.env variable for this particular function.(cellSize, extent) Example: {“exportAllTiles” : False, “startIndex”: 0 }
input_mask_polygons	Optional `FeatureLayer`. The feature layer that delineates the area where image chips will be created. Only image chips that fall completely within the polygons will be created.
rotation_angle	Optional float. The rotation angle that will be used to generate additional image chips. An image chip will be generated with a rotation angle of 0, which means no rotation. It will then be rotated at the specified angle to create an additional image chip. The same training samples will be captured at multiple angles in multiple image chips for data augmentation. The default rotation angle is 0.
reference_system	Optional string. Specifies the type of reference system to be used to interpret the input image. The reference system specified should match the reference system used to train the deep learning model. MAP_SPACE : The input image is in a map-based coordinate system. This is the default. IMAGE_SPACE : The input image is in image space, viewed from the direction of the sensor that captured the image, and rotated such that the tops of buildings and trees point upward in the image. PIXEL_SPACE : The input image is in image space, with no rotation and no distortion.
process_all_raster_items	Optional bool. Specifies how all raster items in an image service will be processed. False : all raster items in the image service will be mosaicked together and processed. This is the default. True : all raster items in the image service will be processed as separate images.
blacken_around_feature	Optional bool. Specifies whether to blacken the pixels around each object or feature in each image tile. This parameter only applies when the metadata format is set to Labeled_Tiles and an input feature class or classified raster has been specified. False : Pixels surrounding objects or features will not be blackened. This is the default. True : Pixels surrounding objects or features will be blackened.
fix_chip_size	Optional bool. Specifies whether to crop the exported tiles such that they are all the same size. This parameter only applies when the metadata format is set to Labeled_Tiles and an input feature class or classified raster has been specified. True : Exported tiles will be the same size and will center on the feature. This is the default. False : Exported tiles will be cropped such that the bounding geometry surrounds only the feature in the tile.
additional_input_raster	Optional `ImageryLayer`/`Raster`/`Item`/String (URL). An additional input imagery source that will be used for image translation methods. This parameter is valid when the metadata_format parameter is set to Classified_Tiles, Export_Tiles, or CycleGAN.
input_instance_data	Optional. The training sample data collected that contains classes for instance segmentation. The input can also be a point feature without a class value field or an integer raster without any class information. This parameter is only valid when the metadata_format parameter is set to Panoptic_Segmentation.
instance_class_value_field	Optional string. The field that contains the class values for instance segmentation. If no field is specified, the tool will use a value or class value field, if one is present. If the feature does not contain a class field, the tool will determine that all records belong to one class. This parameter is only valid when the metadata_format parameter is set to Panoptic_Segmentation.
min_polygon_overlap_ratio	Optional float. The minimum overlap percentage for a feature to be included in the training data. If the percentage overlap is less than the value specified, the feature will be excluded from the training chip, and will not be added to the label file. The percent value is expressed as a decimal. For example, to specify an overlap of 20 percent, use a value of 0.2. The default value is 0, which means that all features will be included. This parameter improves the performance of the tool and also improves inferencing. The speed is improved since less training chips are created. Inferencing is improved since the model is trained to only detect large patches of objects and ignores small corners of features. This parameter is honoured only when the input_class_data parameter value is a feature service.
gis	Optional `GIS` . The GIS on which this tool runs. If not specified, the active GIS is used.
estimate	Keyword only parameter. Optional Boolean. If True, the number of credits needed to run the operation will be returned as a float. Available only on ArcGIS Online
future	Keyword only parameter. Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously.

Returns: Output string containing the location of the exported training data

export_point_dataset

arcgis.learn.export_point_dataset(data_path, output_path, block_size=50.0, max_points=8192, extra_features=[], **kwargs): Note: This function has been deprecated starting from ArcGIS API for Python version 1.9.0. Export data using Prepare Point Cloud Training Data tool available in 3D Analyst Extension from ArcGIS Pro 2.8 onwards.

estimate_batch_size

arcgis.learn.estimate_batch_size(model, mode='train', **kwargs)

Function to calculate estimated batch size based on GPU capacity, size of model and data.

Parameter	Description
model	Required arcgis.learn imagery model. Model instance for which batch size should be estimated. Not supported for text, tabular, timeseries or tracking models such as FullyConnectedNetwork, MLModel, TimeSeriesModel, SiamMask, PSETAE and EfficientDet models.
mode	Optional string. Default train. The mode for which batch size is estimated. Supported ‘train’ and ‘eval’ mode for calculating batch size in training mode and evaluation mode respectively. Note: max_batchsize is capped at 1024 for train and eval mode and recommended_batchsize is capped at 64 for train mode.

Returns: Named tuple of recommended_batchsize and max_batchsize

prepare_data

arcgis.learn.prepare_data(path, class_mapping=None, chip_size=224, val_split_pct=0.1, batch_size=64, transforms=None, collate_fn=<function _bb_pad_collate>, seed=42, dataset_type=None, resize_to=None, working_dir=None, **kwargs)

Prepares a data object from training sample exported by the Export Training Data tool in ArcGIS Pro or Image Server, or training samples in the supported dataset formats. This data object consists of training and validation data sets with the specified transformations, chip size, batch size, split percentage, etc.

Parameter	Description
path	Required string. Path to data directory or a list of paths.
class_mapping	Optional dictionary. Mapping from id to its string label.
chip_size	Optional integer, default 224. Size of the image to train the model. Images are cropped to the specified chip_size. If image size is less than chip_size, the image size is used as chip_size. A chip size that is a multiple of 32 pixels is recommended. Not supported for SuperResolution, SiamMask, WNet_cGAN, Pix2Pix and CycleGAN.
val_split_pct	Optional float. Percentage of training data to keep as validation.
batch_size	Optional integer. Default 64. Batch size for mini batch gradient descent (Reduce it if getting CUDA Out of Memory Errors). Batch size is required to be greater than 1. If None is provided, a recommended batch size is used. This is estimated based on GPU capacity, size of model and data. To explicitly find the recommended batch_size, use arcgis.learn.estimate_batch_size() method.
transforms	Optional tuple. Fast.ai transforms for data augmentation of training and validation datasets respectively (We have set good defaults which work for satellite imagery well). If transforms is set to False no transformation will take place and chip_size parameter will also not take effect. If the dataset_type is ‘PointCloud’ and ‘PointCloudOD’, use `Transform3d`.
collate_fn	Optional function. Passed to PyTorch to collate data into batches(usually default works).
seed	Optional integer. Random seed for reproducible train-validation split.
dataset_type	Optional string. `prepare_data()` function will infer the dataset_type on its own if it contains a map.txt file. If the path does not contain the map.txt file pass one of ‘PASCAL_VOC_rectangles’, ‘KITTI_rectangles’, ‘Imagenet’. This parameter is mandatory for dataset ‘PointCloud’, ‘PointCloudOD’, ‘ImageCaptioning’, ‘ChangeDetection’, ‘WNet_cGAN’ and ‘ObjectTracking’. Note: For details on dataset_type please refer to this link.
resize_to	Optional integer or tuple of integers. A tuple should be of the form (height, width). Resize the images to a given size. Works only for “PASCAL_VOC_rectangles”, “Labelled_Tiles”, “superres” and “Imagenet”.First resizes the image to the given size and then crops images of size equal to chip_size. Note: If resize_to is less than chip_size, the resize_to is used as chip_size.
working_dir	Optional string. Sets the default path to be used as a prefix for saving trained models and checkpoints.

Keyword Arguments

Parameter	Description
n_masks	Optional int. Default value is 30. Required for MaXDeepLab panoptic segmentation model. It represents the max number of class labels and instances any image can contain. To compute the exact value for your dataset, use the `compute_n_masks()` method available with MaXDeepLab model.
downsample_factor	Optional float. Factor to downsample the images for image SuperResolution. for example: if value is 2 and image size 256x256, it will create label images of size 128x128. Default is 4
min_points	For dataset_type=’PointCloud’ and ‘PointCloudOD’: Optional int. Filtering based on minimum number of points in a block. Set min_points=1000 to filter out blocks with less than 1000 points. For dataset_type=’PSETAE’: Optional int. Number of pixels equal to or multiples of 64 to sample from the each masked region of training data i.e. 64, 128 etc.
extra_features	Optional List. Contains a list of strings which mentions extra features to be used for training, applicable with dataset_type ‘PointCloud’ and ‘PointCloudOD’. By default only x, y, and z are considered for training irrespective of what features were exported. For example: [‘intensity’, ‘numberOfReturns’, ‘returnNumber’, ‘red’, ‘green’, ‘blue’, ‘nearInfrared’].
remap_classes	Optional dictionary {int:int}. Mapping from class values to user defined values, in both training and validation data. For dataset_type=’PointCloud’: It will remap LAS classcode structure. For example: {1:3, 2:4} will remap LAS classcode 1 to 3 and classcode 2 to 4. For dataset_type=’PointCloudOD’: It will remap object class ids. When this parameter is set as remap_classes={5:3, 2:4}, then ‘5’ and 2 class values will be considered as ‘3’, and ‘4’, respectively.
classes_of_interest	Optional list of int. For dataset_type=’PointCloud’: This will filter training blocks based on classes_of_interest. If we have “1, 3, 5, 7” LAS classcodes in our dataset, but we are mainly interested in 1 and 3 classcodes, Set classes_of_interest=[1,3]. Only those blocks will be considered for training which either have 1 or 3 LAS classcodes in them, rest of the blocks will be filtered out. If remapping of rest of the classcodes is required, set background_classcode to some value. For dataset_type=’PointCloudOD’: This will filter training blocks based on classes_of_interest. If we have “2, 3, 10, 16” object classes in the 3d feature class, but we are mainly interested in 2 and 10 object classes, Set classes_of_interest=[2,10]. Only those blocks will be considered for training which either have 2 or 10 object classes in them, the rest of the blocks will be filtered out. Set background_classcode as True to discard other classes. Note: classes_of_interest is applied on the remapped class structure, if remap_classes is also used.
background_classcode	This parameter is only applicable when classes_of_interest is specified. For dataset_type=’PointCloud’: Optional int. Default: None. This will remap other class values, except classes_of_interest to background_classcode. For dataset_type=’PointCloudOD’: Optional Bool. Default: False. If set to ‘True’, only classes_of_interest class values will be considered and rest of the class values will be discarded.
stratify	Optional boolean, default False. If True, prepare_data will try to maintain the class proportion in train and validation data according to the val_split_pct. Default value feature classification is True. Default value pixel classification is False. Note: Applies to single label feature classification, object detection and pixel classification.
timesteps_of_interest	Optional list. List of time steps of interest. This will filter multi-temporal timesereis based on timesteps_of_interest. If the dataset have time-steps [0, 1, 2, 3], but we are mainly interested in 0, 1 and 2, Set timesteps_of_interest=[0,1,2]. Only those time-steps will be considered for training, rest of the time-steps will be filtered out. Applicable only for dataset_type=’PSETAE’.
channels_of_interest	Optional list. List of spectral bands/channels of interest. This will filter out bands from rasters of multi-temporal timeseries based on channels_of_interest list. If we have bands [0,1,2,3,4] in our dataset, but we are mainly interested in 0, 1 and 2, Set channels_of_interest=[0,1,2]. Only those spectral bands will be considered for training. Applicable only for dataset_type=’PSETAE’.
n_temporal	Required int. Number of temporal observations or time steps. Applicable only for dataset_type=’PSETAE’.
n_temporal_dates	Required list of strings. The dates of that observations will be used for the positional encoding and should be stored as a list of dates strings in YYYY-MM-DD format. For example, If we have stacked imagery of n bands each from two dates then, [‘YYYY-MM-DD’,’YYYY-MM-DD’]. Applicable only for dataset_type=’PSETAE’.
num_workers	Optional int. Default `0`. number of subprocesses to use for data loading on the Windows operating system. `0` means that the data will be loaded in the main process.
forecast_timesteps	Required int. Default set to 1. How far the model should forecast into the future. A forecast timestep is the interval at which predictions are made, For example, If we have 8-hourly data point and we want to make a 8 hr, 16 hr, 24 hr forecast, forecast timesteps is set to 1, 2, 3 respectively and so on. In case of hourly and monthly data point, for forecasts of 1, 2, 3 hr/month, forecast timestep is set to 1, 2, 3 respectively and so on. Applicable only for climaX model architecuture.
hrs_each_step	Optional int. Default set to 1 (hrs). Number of hours in which data is collected, for example, if you have 8-hourly, hourly, montly, daily then, hrs_each_step is to be set to 8, 1, 720 (30 days * 24), 24 hrs respectively. Applicable only for climaX model architecuture.

Returns: data object

prepare_tabulardata

arcgis.learn.prepare_tabulardata(input_features=None, variable_predict=None, explanatory_variables=None, explanatory_rasters=None, date_field=None, cell_sizes=[3, 4, 5, 6, 7], distance_features=None, preprocessors=None, val_split_pct=0.1, seed=42, batch_size=64, index_field=None, working_dir=None, **kwargs)

Prepares a tabular data object from input_features and optionally rasters.

Parameter	Description
input_features	Optional `FeatureLayer` Object or spatially enabled dataframe. This contains features denoting the value of the dependent variable. Leave empty for using rasters with MLModel.
variable_predict	Optional String or List, denoting the field_names of the variable to predict. Keep none for unsupervised training using ML Model. For timeseries it will work for continuous variable. As of now we support only binary classification in fairness evaluation.
explanatory_variables	Optional list containing field names from input_features By default the field type is continuous. To override field type to categorical, pass a 2-sized tuple in the list containing: field to be taken as input from the input_features. 2. True/False denoting Categorical/Continuous variable. If the field is text, the value should be ‘text’ and if the field is image path, the value should be ‘image’. For example: [“Field_1”, (“Field_2”, True)] or [“Field_1”, (“Field_3”, ‘text’)] Here Field_1 is treated as continuous and Field_2 as categorical and Field_3 as Text
explanatory_rasters	Optional list containing Raster objects. By default the rasters are continuous. To mark a raster categorical, pass a 2-sized tuple containing: Raster object. True/False denoting Categorical/Continuous variable. For example: [raster_1, (raster_2, True)] Here raster_1 is treated as continuous and raster_2 as categorical. To select only specific bands of raster, pass 2/3 sized tuple containing: Raster object. True/False denoting Categorical/Continuous variable. Tuple holding the indexes of the bands to be used. For example: [raster_1, (raster_2, True,(0,)),(raster_3, (0,1,2))] Here bands with indexes 0 will be chosen from raster_2 and it will be treated as categorical variable, bands with indexes 0,1,2 will be chosen from raster_3 and they will be treated as continuous.
date_field	Optional field_name. This field contains the date in the input_features. The field type can be a string or date time field. If specified, the field will be split into Year, month, week, day, dayofweek, dayofyear, is_month_end, is_month_start, is_quarter_end, is_quarter_start, is_year_end, is_year_start, hour, minute, second, elapsed and these will be added to the prepared data as columns. All fields other than elapsed and dayofyear are treated as categorical.
cell_sizes	Size of H3 cells (specified as H3 resolution) for spatially aggregating input features and passing in the cell ids as additional explanatory variables to the model. If a spatial dataframe is passed as input_features, ensure that the spatial reference is 4326, and the geometry type is Point. Not applicable when explanatory_rasters are provided. Not applicable for MLModel.
distance_features	Optional list of `FeatureLayer` objects. Distance is calculated from features in these layers to features in input_features. Nearest distance to each feature is added in the prepared data. Field names in the prepared data added are “NEAR_DIST_1”, “NEAR_DIST_2” etc.
preprocessors	For FullyConnectedNetworks: All the transforms are applied by default and hence users need not pass any additional transforms/preprocessors. For MLModel which uses Scikit-learn transforms: Supply a column transformer object. Supply a list of tuple, For example: [(‘Col_1’, ‘Col_2’, Transform1()), (‘Col_3’, Transform2())] Categorical data is by default encoded. If nothing is specified, default transforms are applied to fill missing values and normalize categorical data. For Raster use raster.name for the first band, raster.name_1 for 2nd band, raster.name_2 for 3rd and so on.
val_split_pct	Optional float. Percentage of training data to keep as validation. By default 10% data is kept for validation.
seed	Optional integer. Random seed for reproducible train-validation split. Default value is 42.
batch_size	Optional integer. Batch size for mini batch gradient descent (Reduce it if getting CUDA Out of Memory Errors). Default value is 64.
index_field	Optional string. Field Name in the input features which will be used as index field for the data. Used for Time Series, to visualize values on the x-axis.
working_dir	Optional string. Sets the default path to be used as a prefix for saving trained models and checkpoints.

Keyword Arguments

Parameter	Description
stratify	Optional boolean. If True, prepare_tabulardata will try to maintain the class proportion in train and validation data according to the val_split_pct. Default value is False. Note Applies to classification problems.
random_split	Optional boolean. sets the behaviour of train and validation split to random or last n steps. If set to True then random sampling will be performed. Otherwise, last n steps will be used as validation. val_split_pct will determine the number the records for validation. Default value is True Note Applies to timeseries

Returns: TabularData object

prepare_textdata

arcgis.learn.prepare_textdata(path, task, text_columns=None, label_columns=None, train_file='train.csv', valid_file=None, val_split_pct=0.1, seed=42, batch_size=8, process_labels=False, remove_html_tags=False, remove_urls=False, working_dir=None, dataset_type=None, class_mapping=None, **kwargs)

Prepares a text data object from the files present at data folder

Parameter	Description
path	Required directory path. The directory path where the training and validation files are present.
task	Required string. The task for which the dataset is prepared. Available choice at this point is “classification”, “sequence_translation” or “entity_recognition”.
text_columns	Optional string. This parameter is mandatory when task is “classification” or “sequence_translation”. This parameter is mandatory when task is entity_recognition task with input dataset_type as csv. The column that will contain the input text.
label_columns	Optional list. This parameter is mandatory when task is “classification” or “sequence_translation”. The list of columns denoting the class label/translated text to predict. Provide a list of columns in case of multi-label classification problem.
train_file	Optional string. The file name containing the training data. Supported file formats/extensions are .csv and .tsv Default value is train.csv
valid_file	Optional string. The file name containing the validation data. Supported file formats/extensions are .csv and .tsv. Default value is None. If None then some portion of the training data will be kept for validation (based on the value of val_split_pct parameter)
val_split_pct	Optional float. Percentage of training data to keep as validation. By default 10% data is kept for validation.
seed	Optional integer. Random seed for reproducible train-validation split. Default value is 42.
batch_size	Optional integer. Batch size for mini batch gradient descent (Reduce it if getting CUDA Out of Memory Errors). Default value is 16.
process_labels	Optional boolean. If true, default processing functions will be called on label columns as well. Default value is False.
remove_html_tags	Optional boolean. If true, remove html tags from text. Default value is False.
remove_urls	Optional boolean. If true, remove urls from text. Default value is False.
working_dir	Optional string. Sets the default path to be used as a prefix for saving trained models and checkpoints.
dataset_type	Optional list. This parameter is mandatory when task is “entity_recognition” Accepted data format for this model are - ‘ner_json’,’BIO’ or ‘LBIOU’, ‘csv’ For csv dataset type. If an entity has multiple values. It should be separated by ,.
class_mapping	Optional dictionary. Mapping from id to its string label. For dataset_type=IOB, BILUO or ner_json: Provide address field as class mapping in below format: class_mapping={‘address_tag’:’address_field’}. Field defined as ‘address_tag’ will be treated as a location. In cases where trained model extracts multiple locations from a single document, that document will be replicated for each location.

Keyword Arguments

Parameter	Description
stratify	Optional boolean. If True, prepare_textdata will try to maintain the class proportion in train and validation data according to the val_split_pct. The default value is True. Note Applies only to single-label text classification.
encoding	Optional string. Applicable only when task is entity_recognition: The encoding to read the csv/json file. Default is ‘UTF-8’

Returns: TextData object

Transform3d

class arcgis.learn.Transform3d(rotation=[2.5, 2.5, 45], scaling=5, jitter=0.0, **kwargs)

Create transformations for 3D datasets, that can be used in prepare_data() to apply data augmentation with a 50% probability. Applicable for dataset_type=’PointCloud’ and dataset_type=’PointCloudOD’.

Parameter	Description
rotation	An optional list of float. It defines a value in degrees for each X, Y, and Z, dimensions which will be used to rotate a block around the X, Y, and Z, axes. Example: A value of [2, 3, 180] means a random value for each X, Y, and Z will be selected between, [-2, 2], [-3, 3], and [-180, 180], respectively. The block will rotate around the respective axis as per the selected random value. Note: For dataset_type=’PointCloudOD’, rotation around the X and Y axes will not be considered. Default: [2.5, 2.5, 45]
scaling	An optional float. It defines a percentage value, that will be used to apply scaling transformation to a block. Example: A value of 5 means, for each X, Y, and Z, dimensions a random value will be selected within the range of [0, 5], where the block might be scaled up or scaled down randomly, in the respective dimension. Note: For dataset_type=’PointCloudOD’, the same scale percentage in all three directions is considered. Default: 5
jitter	Optional float within [0, 1]. It defines a value in meters, which is used to add random variations in X, Y, and Z of all points. Example: if the value provided is 0.1 then within the range of [-0.1, 0.1] a random value is selected, The selected value is then added to the point’s X coordinate. Similarly, it is applied for Y and Z coordinates. Note: Only applicable for dataset_type=’PointCloud’. Default: 0.0.

Returns: Transform3d object

Automated Machine Learning

AutoML

class arcgis.learn.AutoML(data=None, total_time_limit=3600, mode='Basic', algorithms=None, eval_metric='auto', n_jobs=1, ml_task='auto', **kwargs)

Automates the process of model selection, training and hyperparameter tuning of machine learning models within a specified time limit. Based upon MLJar(https://github.com/mljar/mljar-supervised/) and scikit-learn.

Note that automated machine learning support is provided only for supervised learning. Refer https://supervised.mljar.com/

Parameter	Description
data	Required TabularDataObject. Returned data object from `prepare_tabulardata()` function.
total_time_limit	Optional Int. The total time limit in seconds for AutoML training. Default is 3600 (1 Hr)
mode	Optional Str. Can be {Basic, Intermediate, Advanced}. This parameter defines the goal of AutoML and how intensive the AutoML search will be. Basic : To to be used when the user wants to explain and understand the data. Uses 75%/25% train/test split. Uses the following models: Baseline, Linear, Decision Tree, Random Trees, XGBoost, Neural Network, and Ensemble. Has full explanations in reports: learning curves, importance plots, and SHAP plots. Intermediate : To be used when the user wants to train a model that will be used in real-life use cases. Uses 5-fold CV (Cross-Validation). Uses the following models: Linear, Random Trees, LightGBM, XGBoost, CatBoost, Neural Network, and Ensemble. Has learning curves and importance plots in reports. Advanced : To be used for machine learning competitions (maximum performance). Uses 10-fold CV (Cross-Validation). Uses the following models: Decision Tree, Random Trees, Extra Trees, XGBoost, CatBoost, Neural Network, Nearest Neighbors, Ensemble, and Stacking.It has only learning curves in the reports. Default is Basic
algorithms	Optional. List of str. The list of algorithms that will be used in the training. The algorithms can be: Linear, Decision Tree, Random Trees, Extra Trees, LightGBM, Xgboost, Neural Network
eval_metric	Optional Str. The metric to be used to compare models. Possible values are: For binary classification - logloss (default), auc, f1, average_precision, accuracy. For multiclass classification - logloss (default), f1, accuracy For regression - rmse (default), mse, mae, r2, mape, spearman, pearson Note - If there are only 2 unique values in the target, then binary classification is performed, If number of unique values in the target is between 2 and 20 (included), then multiclass classification is performed, In all other cases, regression is performed on the dataset.
n_jobs	Optional. Int. Number of CPU cores to be used. By default, it is set to 1.Set it to -1 to use all the cores.

kwargs

sensitive_variables	Optional. List of strings. Variables in the feature class/dataframe which are sensitive and prone to model bias. Ex - [‘sex’,’race’] or [‘nationality’]
fairness_metric	Optional. String. Name of fairness metric based on which fairness optimization should be done on the evaluated models. Available metrics for binary classification are ‘demographic_parity_difference’ , ‘demographic_parity_ratio’, ‘equalized_odds_difference’, ‘equalized_odds_ratio’. ‘demographic_parity_ratio’ is the default. Available metrics for regression are ‘group_loss_ratio’ (Default) and ‘group_loss_difference’.
fairness_threshold	Optional. Float. Required when the chosen metric is group_loss_difference The threshold value for fairness metric. Default values are as follows: - for demographic_parity_difference the metric value should be below 0.25, - for demographic_parity_ratio the metric value should be above 0.8, - for equalized_odds_difference the metric value should be below 0.25, - for equalized_odds_ratio the metric value should be above 0.8. - for group_loss_ratio the metric value should be above 0.8. - for group_loss_difference the metric value should be below 0.25,
privileged_groups	Optional. List. List of previleged groups in the sensitive attribute. For example, in binary classification task, a privileged group is the one with the highest selection rate. Example value: [{“sex”: “Male”}]
underprivileged_groups	Optional. List. List of underprivileged groups in the sensitive attribute. For example, in binary classification task, an underprivileged group is the one with the lowest selection rate. Example value: [{“sex”: “Female”}]

Returns: AutoML Object

copy_and_overwrite(from_path, to_path)

fairness_score(sensitive_feature, fairness_metrics=None, visualize=False)

Shows sample results for the model.

Returns: tuple/dataframe

fit(sample_weight=None): Fits the AutoML model.

classmethod from_model(emd_path)

Creates an AutoML Model Object from an Esri Model Definition (EMD) file. The model object created can only be used for inference on a new dataset and cannot be retrained.

Parameter	Description
emd_path	Required string. Path to Esri Model Definition file.

Returns: AutoML Object

get_ml_task(all_labels)

predict(input_features=None, explanatory_rasters=None, datefield=None, distance_features=None, output_layer_name='Prediction Layer', gis=None, prediction_type='features', output_raster_path=None, match_field_names=None, cell_sizes=[3, 4, 5, 6, 7], confidence=True, get_local_explanations=False, **kwargs)

Predict on data from feature layer, dataframe and or raster data.

Parameter	Description
input_features	Optional `FeatureLayer` or spatial dataframe. Required if prediction_type=’features’. Contains features with location and some or all fields required to infer the dependent variable value.
explanatory_rasters	Optional list. Required if prediction_type=’raster’. Contains a list of raster objects containing some or all fields required to infer the dependent variable value.
datefield	Optional string. Field name from feature layer that contains the date, time for the input features. Same as `prepare_tabulardata()`.
cell_sizes	Size of H3 cells (specified as H3 resolution) for spatially aggregating input features and passing in the cell ids as additional explanatory variables to the model. If a spatial dataframe is passed as input_features, ensure that the spatial reference is 4326, and the geometry type is Point. Not applicable when explanatory_rasters are provided.
distance_features	Optional List of `FeatureLayer` objects. These layers are used for calculation of field “NEAR_DIST_1”, “NEAR_DIST_2” etc in the output dataframe. These fields contain the nearest feature distance from the input_features. Same as `prepare_tabulardata()` .
output_layer_name	Optional string. Used for publishing the output layer.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
prediction_type	Optional String. Set ‘features’ or ‘dataframe’ to make output feature layer predictions. With this feature_layer argument is required. Set ‘raster’, to make prediction raster. With this rasters must be specified.
output_raster_path	Optional path. Required when prediction_type=’raster’, saves the output raster to this path.
match_field_names	Optional dictionary. Specify mapping of field names from prediction set to training set. For example: { “Field_Name_1”: “Field_1”, “Field_Name_2”: “Field_2” }
confidence	Optional Bool. Set confidence to True to get prediction confidence for classification use cases.Default is True.

Returns: FeatureLayer if prediction_type=’features’, dataframe for prediction_type=’dataframe’ else creates an output raster.

predict_proba()

Returns: output from AutoML’s model.predict_proba() with prediction probability for the training data

report()

Returns: a report of the different models trained by AutoML along with their performance.

save(path)

Saves the model in the path specified. Creates an Esri Model and a dlpk. Uses pickle to save the model and transforms.

Returns: path

score()

Returns: output from AutoML’s model.score(), R2 score in case of regression and Accuracy in case of classification.

show_results(rows=5)

Shows sample results for the model.

Returns: dataframe

AutoDL

class arcgis.learn.AutoDL(data=None, total_time_limit=2, mode='basic', network=None, verbose=True, **kwargs)

Automates the process of model selection, training and hyperparameter tuning of arcgis.learn supported deep learning models within a specified time limit.

Parameter	Description
data	Required ImageryDataObject. Returned data object from `prepare_data()` function.
total_time_limit	Optional Int. The total time limit in hours for AutoDL training. Default is 2 Hr.
mode	Optional String. Can be “basic” or “advanced”. basic : To be used when the user wants to train all selected networks. advanced : To be used when the user wants to tune hyper parameters of two best performing models from basic mode.
network	Optional List of str. The list of models that will be used in the training. For eg: Supported Object Detection models: [“SingleShotDetector”, “RetinaNet”, “FasterRCNN”, “YOLOv3”, “MaskRCNN”, “DETReg” ,”ATSS”, “CARAFE”, “CascadeRCNN”, “CascadeRPN”, “DCN”, ‘Detectors’, ‘DoubleHeads’, ‘DynamicRCNN’, ‘EmpiricalAttention’, ‘FCOS’, ‘FoveaBox’, ‘FSAF’, ‘GHM’, ‘LibraRCNN’, ‘PaFPN’, ‘PISA’, ‘RegNet’,’RepPoints’, ‘Res2Net’, ‘SABL’, ‘VFNet’] Supported Pixel Classification models: [“DeepLab”, “UnetClassifier”, “PSPNetClassifier”, “ANN”, “APCNet”, “CCNet”, “CGNet”, “HRNet”, ‘DeepLabV3Plus’, ‘DMNet’, ‘DNLNet’, ‘FastSCNN’, ‘FCN’, ‘GCNet’, ‘MobileNetV2’, ‘NonLocalNet’,’OCRNet’, ‘PSANet’, ‘SemFPN’, ‘UperNet’]
verbose	Optional Boolean. To be used to display logs while training the models. Default is True.

Returns: AutoDL Object

average_precision_score(): Calculates the average of the “average precision score” of all classes for selected networks

fit(**kwargs): Train the selected networks for the specified number of epochs and using the specified learning rates

mIOU(): Calculates the mIOU of all classes for selected networks

report(allow_plot=False): returns a HTML report of the different models trained by AutoDL along with their performance.

score(allow_plot=False): returns output from AutoDL’s model.score(), “average precision score” in case of detection and accuracy in case of classification.

show_results(rows=5, threshold=0.25, **kwargs)

Shows sample results for the model.

Parameter	Description
rows	Optional number of rows. By default, 5 rows are displayed.

Returns: dataframe

supported_classification_models(): Supported classification models.

supported_detection_models(): Supported detection models.

ImageryModel

class arcgis.learn.ImageryModel

Imagery Model is used to fine tune models trained using AutoDL

available_metrics(): List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

average_precision_score()

Computes average precision on the validation set for each class.

Parameter	Description
detect_thresh	Optional float. The probability above which a detection will be considered for computing average precision.
iou_thresh	Optional float. The intersection over union threshold with the ground truth labels, above which a predicted bounding box will be considered a true positive.
mean	Optional bool. If False returns class-wise average precision otherwise returns mean average precision.

Returns: dict if mean is False otherwise float

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs while using the specified learning rates

Parameter	Description
epochs	Optional integer. Number of cycles of training on the data. Increase it if the model is underfitting. Default value is 10.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use `{model_name}.available_metrics` to list the available metrics to set here.

load(path, data)

Loads a compatible saved model for inferencing or fine tuning from the disk, which can be used to further fine tune the models saved using AutoDL.

Parameter	Description
path	Required string. Path to Esri Model Definition(EMD) or DLPK file.
data	Required ImageryDataObject. Returned data object from `prepare_data()` function.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

mIOU()

Computes mean IOU on the validation set for each class.

Parameter	Description
mean	Optional bool. If False returns class-wise mean IOU, otherwise returns mean iou of all classes combined.
show_progress	Optional bool. Displays the progress bar if True.

Returns: dict if mean is False otherwise float

plot_losses(): Plot validation and training losses after fitting the model.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector`, `FeatureClassifier` and `RetinaNet` . `torchscript` format is supported by `SiamMask` . For usage of SiamMask model in ArcGIS Pro 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, **kwargs)

Displays the results of a trained model on a part of the validation set.

rows	Optional int. Number of rows of results to be displayed.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

Object Classification Models

FeatureClassifier

class arcgis.learn.FeatureClassifier(data, backbone='resnet34', pretrained_path=None, mixup=False, oversample=False, backend='pytorch', *args, **kwargs)

Creates an image classifier to classify the area occupied by a geographical feature based on the imagery it overlaps with.

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
backbone	Optional string. Backbone convolutional neural network model used for feature extraction, which is `resnet34` by default. Supported backbones: ResNet family and specified Timm models(experimental support) from `backbones()`.
pretrained_path	Optional string. Path where pre-trained model is saved.
mixup	Optional boolean. If set to True, it creates new training images by randomly mixing training set images. The default is set to False.
oversample	Optional boolean. If set to True, it oversamples unbalanced classes of the dataset during training. Not supported with MultiLabel dataset.
backend	Optional string. Controls the backend framework to be used for this model, which is ‘pytorch’ by default. valid options are “`pytorch`”, “`tensorflow`”

Returns: FeatureClassifier Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

static backbones(): Supported list of backbones for this model.

categorize_features(feature_layer, raster=None, class_value_field='class_val', class_name_field='prediction', confidence_field='confidence', cell_size=1, coordinate_system=None, predict_function=None, batch_size=64, overwrite=False)

Deprecated since version 1.7.1: Please use classify_objects() instead

Categorizes each feature by classifying its attachments or an image of its geographical area (using the provided Imagery Layer) and updates the feature layer with the prediction results in the output_label_field. Deprecated, Use the Classify Objects Using Deep Learning tool or classify_objects()

Parameter	Description
feature_layer	Required. Public `FeatureLayer` or path of local feature class for classification with read, write, edit permissions.
raster	Optional. `ImageryLayer` or path of local raster to be used for exporting image chips. (Requires arcpy)
class_value_field	Required string. Output field to be added in the layer, containing class value of predictions.
class_name_field	Required string. Output field to be added in the layer, containing class name of predictions.
confidence_field	Optional string. Output column name to be added in the layer which contains the confidence score.
cell_size	Optional float. Cell size to be used for exporting the image chips.
coordinate_system	Optional. Cartographic Coordinate System to be used for exporting the image chips.
predict_function	Optional list of tuples. Used for calculation of final prediction result when each feature has more than one attachment. The `predict_function` takes as input a list of tuples. Each tuple has first element as the class predicted and second element is the confidence score. The function should return the final tuple classifying the feature and its confidence.
batch_size	Optional integer. The no of images or tiles to process in a single go. The default value is 64.
overwrite	Optional boolean. If set to True the output fields will be overwritten by new values. The default value is False.

Returns: Boolean : True if operation is successful, False otherwise

classify_features(feature_layer, labeled_tiles_directory, input_label_field, output_label_field, confidence_field=None, predict_function=None)

Deprecated in ArcGIS version 1.9.1 and later: Use the Classify Objects Using Deep Learning tool or classify_objects()

Classifies the exported images and updates the feature layer with the prediction results in the output_label_field. Works with RGB images only.

Parameter	Description
feature_layer	Required. `FeatureLayer` for classification.
labeled_tiles_directory	Required. Folder structure containing images and labels folder. The chips should have been generated using the export training data tool in the Labeled Tiles format, and the labels should contain the OBJECTIDs of the features to be classified.
input_label_field	Required. Value field name which created the labeled tiles. This field should contain the OBJECTIDs of the features to be classified. In case of attachments this field is not used.
output_label_field	Required. Output column name to be added in the layer which contains predictions.
confidence_field	Optional. Output column name to be added in the layer which contains the confidence score.
predict_function	Optional. Used for calculation of final prediction result when each feature has more than one attachment. The `predict_function` takes as input a list of tuples. Each tuple has first element as the class predicted and second element is the confidence score. The function should return the final tuple classifying the feature and its confidence

Returns: Boolean : True/False if operation is successful

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a Feature classifier from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: FeatureClassifier Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_confusion_matrix(**kwargs)

Plots a confusion matrix of the model predictions to evaluate accuracy kwargs

Parameter	Description
thresh	confidence score threshold for multilabel predictions, defaults to 0.5

plot_hard_examples(num_examples)

Plots the hard examples with their heatmaps.

Parameter	Description
num_examples	Number of hard examples to plot `prepare_data()` function.

plot_losses(): Plot validation and training losses after fitting the model.

predict(img_path, visualize=False, gradcam=False)

Runs prediction on an Image. Works with RGB images only.

Parameter	Description
img_path	Required. Path to the image file to make the predictions on.
visualize	Optional: Set this parameter to True to visualize the image being predicted.
gradcam	Optional: Set this parameter to True to get gradcam visualization to help with explanability of the prediction. If set to True, visualize parameter must also be set to True.

Returns: prediction label and confidence

predict_folder_and_create_layer(folder, feature_layer_name, gis=None, prediction_field='predict', confidence_field='confidence')

Predicts on images present in the given folder and creates a feature layer. The images stored in the folder contain GPS information as part of EXIF metadata. Works with RGB images only.

Parameter	Description
folder	Required String. Folder containing images to inference on.
feature_layer_name	Required String. The name of the feature layer used to publish.
gis	Optional `GIS` Object, the GIS on which this tool runs. If not specified, the active GIS is used.
prediction_field	Optional String. The field name to use to add predictions.
confidence_field	Optional String. The field name to use to add confidence.

Returns: FeatureCollection Object

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, **kwargs)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.

property supported_backbones: Supported list of backbones for this model.

property supported_datasets: Supported dataset types for this model.

static transformer_backbones()

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

Object Detection Models

FasterRCNN

class arcgis.learn.FasterRCNN(data, backbone='resnet50', pretrained_path=None, **kwargs)

Model architecture from https://arxiv.org/abs/1506.01497. Creates a FasterRCNN object detection model, based on https://github.com/pytorch/vision/blob/master/torchvision/models/detection/faster_rcnn.py.

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
backbone	Optional string. Backbone convolutional neural network model used for feature extraction, which is resnet50 by default. Supported backbones: ResNet family and specified Timm models(experimental support) from `backbones()`.
pretrained_path	Optional string. Path where pre-trained model is saved.

kwargs

Parameter	Description
rpn_pre_nms_top_n_train	Optional int. Number of proposals to keep before applying NMS during training. Default: 2000
rpn_pre_nms_top_n_test	Optional int. Number of proposals to keep before applying NMS during testing. Default: 1000
rpn_post_nms_top_n_train	Optional int. Number of proposals to keep after applying NMS during training. Default: 2000
rpn_post_nms_top_n_test	Optional int. Number of proposals to keep after applying NMS during testing. Default: 1000
rpn_nms_thresh	Optional float. NMS threshold used for postprocessing the RPN proposals. Default: 0.7
rpn_fg_iou_thresh	Optional float. Minimum IoU between the anchor and the GT box so that they can be considered as positive during training of the RPN. Default: 0.7
rpn_bg_iou_thresh	Optional float. Maximum IoU between the anchor and the GT box so that they can be considered as negative during training of the RPN. Default: 0.3
rpn_batch_size_per_image	Optional int. Number of anchors that are sampled during training of the RPN for computing the loss. Default: 256
rpn_positive_fraction	Optional float. Proportion of positive anchors in a mini-batch during training of the RPN. Default: 0.5
box_score_thresh	Optional float. During inference, only return proposals with a classification score greater than box_score_thresh Default: 0.05
box_nms_thresh	Optional float. NMS threshold for the prediction head. Used during inference. Default: 0.5
box_detections_per_img	Optional int. Maximum number of detections per image, for all classes. Default: 100
box_fg_iou_thresh	Optional float. Minimum IoU between the proposals and the GT box so that they can be considered as positive during training of the classification head. Default: 0.5
box_bg_iou_thresh	Optional float. Maximum IoU between the proposals and the GT box so that they can be considered as negative during training of the classification head. Default: 0.5
box_batch_size_per_image	Optional int. Number of proposals that are sampled during training of the classification head. Default: 512
box_positive_fraction	Optional float. Proportion of positive proposals in a mini-batch during training of the classification head. Default: 0.25

Returns: FasterRCNN Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

average_precision_score(detect_thresh=0.2, iou_thresh=0.1, mean=False, show_progress=True)

Computes average precision on the validation set for each class.

Parameter	Description
detect_thresh	Optional float. The probability above which a detection will be considered for computing average precision.
iou_thresh	Optional float. The intersection over union threshold with the ground truth labels, above which a predicted bounding box will be considered a true positive.
mean	Optional bool. If False returns class-wise average precision otherwise returns mean average precision.

Returns: dict if mean is False otherwise float

static backbones(): Supported list of backbones for this model.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a FasterRCNN object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: FasterRCNN Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict(image_path, threshold=0.5, nms_overlap=0.1, return_scores=False, visualize=False, resize=False)

Runs prediction on an Image. This method is only supported for RGB images.

Parameter	Description
image_path	Required. Path to the image file to make the predictions on.
threshold	Optional float. The probability above which a detection will be considered valid.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.
return_scores	Optional boolean. Will return the probability scores of the bounding box predictions if True.
visualize	Optional boolean. Displays the image with predicted bounding boxes if True.
resize	Optional boolean. Resizes the image to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the image is resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the image (of the same size as the model was trained on).

Returns: Returns a tuple with predictions, labels and optionally confidence scores if return_scores=True. The predicted bounding boxes are returned as a list of lists containing the xmin, ymin, width and height of each predicted object in each image. The labels are returned as a list of class values and the confidence scores are returned as a list of floats indicating the confidence of each prediction.

predict_video(input_video_path, metadata_file, threshold=0.5, nms_overlap=0.1, track=False, visualize=False, output_file_path=None, multiplex=False, multiplex_file_path=None, tracker_options={'assignment_iou_thrd': 0.3, 'detect_frames': 10, 'vanish_frames': 40}, visual_options={'color': 255, 255, 255, 'fontface': 0, 'show_labels': True, 'show_scores': True, 'thickness': 2}, resize=False)

Runs prediction on a video and appends the output VMTI predictions in the metadata file. This method is only supported for RGB images.

Parameter	Description
input_video_path	Required. Path to the video file to make the predictions on.
metadata_file	Required. Path to the metadata csv file where the predictions will be saved in VMTI format.
threshold	Optional float. The probability above which a detection will be considered.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.
track	Optional bool. Set this parameter as True to enable object tracking.
visualize	Optional boolean. If True a video is saved with prediction results.
output_file_path	Optional path. Path of the final video to be saved. If not supplied, video will be saved at path input_video_path appended with _prediction.
multiplex	Optional boolean. Runs Multiplex using the VMTI detections.
multiplex_file_path	Optional path. Path of the multiplexed video to be saved. By default a new file with _multiplex.MOV extension is saved in the same folder.
tracking_options	Optional dictionary. Set different parameters for object tracking. assignment_iou_thrd parameter is used to assign threshold for assignment of trackers, vanish_frames is the number of frames the object should be absent to consider it as vanished, detect_frames is the number of frames an object should be detected to track it.
visual_options	Optional dictionary. Set different parameters for visualization. show_scores boolean, to view scores on predictions, show_labels boolean, to view labels on predictions, thickness integer, to set the thickness level of box, fontface integer, fontface value from opencv values, color tuple (B, G, R), tuple containing values between 0-255.
resize	Optional boolean. Resizes the video frames to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the video frames are resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the frame (of the same size as the model was trained on).

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, thresh=0.5, nms_overlap=0.1)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.
thresh	Optional float. The probability above which a detection will be considered valid.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.

property supported_backbones: Supported list of backbones for this model.

property supported_datasets: Supported dataset types for this model.

static transformer_backbones()

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

RetinaNet

class arcgis.learn.RetinaNet(data, scales=None, ratios=None, backbone=None, pretrained_path=None, *args, **kwargs)

Creates a RetinaNet Object Detector with the specified zoom scales and aspect ratios. Based on the Fast.ai notebook

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
scales	Optional list of float values. Zoom scales of anchor boxes.
ratios	Optional list of float values. Aspect ratios of anchor boxes.
backbone	Optional string. Backbone convolutional neural network model used for feature extraction, which is resnet50 by default. Supported backbones: ResNet family and specified Timm models(experimental support) from `backbones()`.
pretrained_path	Optional string. Path where pre-trained model is saved.

Returns: RetinaNet Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

average_precision_score(detect_thresh=0.5, iou_thresh=0.1, mean=False, show_progress=True)

Computes average precision on the validation set for each class.

Parameter	Description
detect_thresh	Optional float. The probability above which a detection will be considered for computing average precision.
iou_thresh	Optional float. The intersection over union threshold with the ground truth labels, above which a predicted bounding box will be considered a true positive.
mean	Optional bool. If False returns class-wise average precision otherwise returns mean average precision.

Returns: dict if mean is False otherwise float

static backbones(): Supported list of backbones for this model.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a RetinaNet Object Detector from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: RetinaNet Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict(image_path, threshold=0.5, nms_overlap=0.1, return_scores=True, visualize=False, resize=False, batch_size=1)

Predicts and displays the results of a trained model on a single image. This method is only supported for RGB images.

Parameter	Description
image_path	Required. Path to the image file to make the predictions on.
thresh	Optional float. The probability above which a detection will be considered valid.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.
return_scores	Optional boolean. Will return the probability scores of the bounding box predictions if True.
visualize	Optional boolean. Displays the image with predicted bounding boxes if True.
resize	Optional boolean. Resizes the image to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the image is resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the image (of the same size as the model was trained on).
batch_size	Optional int. Batch size to be used during tiled inferencing. Deafult value 1.

Returns: ‘List’ of xmin, ymin, width, height of predicted bounding boxes on the given image

Runs prediction on a video and appends the output VMTI predictions in the metadata file. This method is only supported for RGB images.

Parameter	Description
input_video_path	Required. Path to the video file to make the predictions on.
metadata_file	Required. Path to the metadata csv file where the predictions will be saved in VMTI format.
threshold	Optional float. The probability above which a detection will be considered.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.
track	Optional bool. Set this parameter as True to enable object tracking.
visualize	Optional boolean. If True a video is saved with prediction results.
output_file_path	Optional path. Path of the final video to be saved. If not supplied, video will be saved at path input_video_path appended with _prediction.
multiplex	Optional boolean. Runs Multiplex using the VMTI detections.
multiplex_file_path	Optional path. Path of the multiplexed video to be saved. By default a new file with _multiplex.MOV extension is saved in the same folder.
tracking_options	Optional dictionary. Set different parameters for object tracking. assignment_iou_thrd parameter is used to assign threshold for assignment of trackers, vanish_frames is the number of frames the object should be absent to consider it as vanished, detect_frames is the number of frames an object should be detected to track it.
visual_options	Optional dictionary. Set different parameters for visualization. show_scores boolean, to view scores on predictions, show_labels boolean, to view labels on predictions, thickness integer, to set the thickness level of box, fontface integer, fontface value from opencv values, color tuple (B, G, R), tuple containing values between 0-255.
resize	Optional boolean. Resizes the video frames to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the video frames are resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the frame (of the same size as the model was trained on).

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, thresh=0.5, nms_overlap=0.1)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.
thresh	Optional float. The probability above which a detection will be considered valid.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.

property supported_backbones: Supported list of backbones for this model.

property supported_datasets: Supported dataset types for this model.

static transformer_backbones()

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

YOLOv3

class arcgis.learn.YOLOv3(data=None, pretrained_path=None, **kwargs)

Creates a YOLOv3 object detector.

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function. YOLOv3 only supports image sizes in multiples of 32 (e.g. 256, 416, etc.)
pretrained_path	Optional string. Path where pre-trained model is saved.

Returns: YOLOv3 Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

average_precision_score(detect_thresh=0.1, iou_thresh=0.1, mean=False, show_progress=True)

Computes average precision on the validation set for each class.

Parameter	Description
detect_thresh	Optional float. The probability above which a detection will be considered for computing average precision. Defaults to 0.1. To be modified according to the dataset and training.
iou_thresh	Optional float. The intersection over union threshold with the ground truth labels, above which a predicted bounding box will be considered a true positive.
mean	Optional bool. If False returns class-wise average precision otherwise returns mean average precision.

Returns: dict if mean is False otherwise float

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a YOLOv3 Object Detector from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: YOLOv3 Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict(image_path, threshold=0.1, nms_overlap=0.1, return_scores=True, visualize=False, resize=False, batch_size=1)

Predicts and displays the results of a trained model on a single image. This method is only supported for RGB images. The image size should at least be 416x416px if using COCO pretrained weights. This method is only supported for RGB images.

Parameter	Description
image_path	Required. Path to the image file to make the predictions on.
threshold	Optional float. The probability above which a detection will be considered valid. Defaults to 0.1. To be modified according to the dataset and training.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.
return_scores	Optional boolean. Will return the probability scores of the bounding box predictions if True.
visualize	Optional boolean. Displays the image with predicted bounding boxes if True.
resize	Optional boolean. Resizes the image to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the image is resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the image (of the same size as the model was trained on).
batch_size	Optional int. Batch size to be used during tiled inferencing. Deafult value 1.

Returns: ‘List’ of xmin, ymin, width, height of predicted bounding boxes on the given image

predict_video(input_video_path, metadata_file, threshold=0.1, nms_overlap=0.1, track=False, visualize=False, output_file_path=None, multiplex=False, multiplex_file_path=None, tracker_options={'assignment_iou_thrd': 0.3, 'detect_frames': 10, 'vanish_frames': 40}, visual_options={'color': 255, 255, 255, 'fontface': 0, 'show_labels': True, 'show_scores': True, 'thickness': 2}, resize=False)

Runs prediction on a video and appends the output VMTI predictions in the metadata file. This method is only supported for RGB images.

Parameter	Description
input_video_path	Required. Path to the video file to make the predictions on.
metadata_file	Required. Path to the metadata csv file where the predictions will be saved in VMTI format.
threshold	Optional float. The probability above which a detection will be considered. Defaults to 0.1. To be modified according to the dataset and training.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.
track	Optional bool. Set this parameter as True to enable object tracking.
visualize	Optional boolean. If True a video is saved with prediction results.
output_file_path	Optional path. Path of the final video to be saved. If not supplied, video will be saved at path input_video_path appended with _prediction.
multiplex	Optional boolean. Runs Multiplex using the VMTI detections.
multiplex_file_path	Optional path. Path of the multiplexed video to be saved. By default a new file with _multiplex.MOV extension is saved in the same folder.
tracking_options	Optional dictionary. Set different parameters for object tracking. assignment_iou_thrd parameter is used to assign threshold for assignment of trackers, vanish_frames is the number of frames the object should be absent to consider it as vanished, detect_frames is the number of frames an object should be detected to track it.
visual_options	Optional dictionary. Set different parameters for visualization. show_scores boolean, to view scores on predictions, show_labels boolean, to view labels on predictions, thickness integer, to set the thickness level of box, fontface integer, fontface value from opencv values, color tuple (B, G, R), tuple containing values between 0-255.
resize	Optional boolean. Resizes the video frames to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the video frames are resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the frame (of the same size as the model was trained on).

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, thresh=0.1, nms_overlap=0.1)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.
thresh	Optional float. The probability above which a detection will be considered valid. Defaults to 0.1. To be modified according to the dataset and training.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.

property supported_backbones: Supported backbones for this model.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

SingleShotDetector

class arcgis.learn.SingleShotDetector(data, grids=None, zooms=[1.0], ratios=[[1.0, 1.0]], backbone=None, drop=0.3, bias=- 4.0, focal_loss=False, pretrained_path=None, location_loss_factor=None, ssd_version=2, backend='pytorch', *args, **kwargs)

Creates a Single Shot Detector with the specified grid sizes, zoom scales and aspect ratios. Based on Fast.ai MOOC Version2 Lesson 9.

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
grids	Required list. Grid sizes used for creating anchor boxes.
zooms	Optional list. Zooms of anchor boxes.
ratios	Optional list of tuples. Aspect ratios of anchor boxes.
backbone	Optional string. Backbone convolutional neural network model used for feature extraction, which is resnet34 by default. Supported backbones: ResNet, DenseNet, VGG families and specified Timm models(experimental support) from `backbones()`.
dropout	Optional float. Dropout probability. Increase it to reduce overfitting.
bias	Optional float. Bias for SSD head.
focal_loss	Optional boolean. Uses Focal Loss if True.
pretrained_path	Optional string. Path where pre-trained model is saved.
location_loss_factor	Optional float. Sets the weight of the bounding box loss. This should be strictly between 0 and 1. This is default None which gives equal weight to both location and classification loss. This factor adjusts the focus of model on the location of bounding box.
ssd_version	Optional int within [1,2]. Use version=1 for arcgis v1.6.2 or earlier
backend	Optional string. Controls the backend framework to be used for this model, which is ‘pytorch’ by default. valid options are ‘pytorch’, ‘tensorflow’

Returns: SingleShotDetector Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

average_precision_score(detect_thresh=0.2, iou_thresh=0.1, mean=False, show_progress=True)

Computes average precision on the validation set for each class.

Parameter	Description
detect_thresh	Optional float. The probability above which a detection will be considered for computing average precision.
iou_thresh	Optional float. The intersection over union threshold with the ground truth labels, above which a predicted bounding box will be considered a true positive.
mean	Optional bool. If False returns class-wise average precision otherwise returns mean average precision.

Returns: dict if mean is False otherwise float

static backbones(): Supported list of backbones for this model.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_emd(data, emd_path)

Creates a Single Shot Detector from an Esri Model Definition (EMD) file.

Parameter	Description
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.
emd_path	Required string. Path to Esri Model Definition file.

Returns: SingleShotDetector Object

classmethod from_model(emd_path, data=None)

Creates a Single Shot Detector from an Esri Model Definition (EMD) file.

Note: Only supported for Pytorch models.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: SingleShotDetector Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict(image_path, threshold=0.5, nms_overlap=0.1, return_scores=False, visualize=False, resize=False, batch_size=1)

Runs prediction on an Image. This method is only supported for RGB images.

Parameter	Description
image_path	Required. Path to the image file to make the predictions on.
threshold	Optional float. The probability above which a detection will be considered valid.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.
return_scores	Optional boolean. Will return the probability scores of the bounding box predictions if True.
visualize	Optional boolean. Displays the image with predicted bounding boxes if True.
resize	Optional boolean. Resizes the image to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the image is resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the image (of the same size as the model was trained on).
batch_size	Optional int. Batch size to be used during tiled inferencing. Deafult value 1.

Returns: ‘List’ of xmin, ymin, width, height of predicted bounding boxes on the given image

Runs prediction on a video and appends the output VMTI predictions in the metadata file. This method is only supported for RGB images.

Parameter	Description
input_video_path	Required. Path to the video file to make the predictions on.
metadata_file	Required. Path to the metadata csv file where the predictions will be saved in VMTI format.
threshold	Optional float. The probability above which a detection will be considered.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.
track	Optional bool. Set this parameter as True to enable object tracking.
visualize	Optional boolean. If True a video is saved with prediction results.
output_file_path	Optional path. Path of the final video to be saved. If not supplied, video will be saved at path input_video_path appended with _prediction.
multiplex	Optional boolean. Runs Multiplex using the VMTI detections.
multiplex_file_path	Optional path. Path of the multiplexed video to be saved. By default a new file with _multiplex.MOV extension is saved in the same folder.
tracking_options	Optional dictionary. Set different parameters for object tracking. assignment_iou_thrd parameter is used to assign threshold for assignment of trackers, vanish_frames is the number of frames the object should be absent to consider it as vanished, detect_frames is the number of frames an object should be detected to track it.
visual_options	Optional dictionary. Set different parameters for visualization. show_scores boolean, to view scores on predictions, show_labels boolean, to view labels on predictions, thickness integer, to set the thickness level of box, fontface integer, fontface value from opencv values, color tuple (B, G, R), tuple containing values between 0-255.
resize	Optional boolean. Resizes the image to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the image is resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the image (of the same size as the model was trained on).

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, thresh=0.5, nms_overlap=0.1)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.
thresh	Optional float. The probability above which a detection will be considered valid.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.

property supported_backbones: Supported list of backbones for this model.

property supported_datasets: Supported dataset types for this model.

static transformer_backbones()

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

MaskRCNN

class arcgis.learn.MaskRCNN(data, backbone=None, pretrained_path=None, pointrend=False, *args, **kwargs)

Model architecture from https://arxiv.org/abs/1703.06870. Creates a MaskRCNN Instance segmentation model, based on https://github.com/pytorch/vision/blob/master/torchvision/models/detection/mask_rcnn.py.

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
backbone	Optional string. Backbone convolutional neural network model used for feature extraction, which is resnet50 by default. Supported backbones: ResNet family and specified Timm models(experimental support) from `backbones()`.
pretrained_path	Optional string. Path where pre-trained model is saved.
pointrend	Optional boolean. If True, it will use PointRend architecture on top of the segmentation head. Default: False. PointRend architecture from https://arxiv.org/pdf/1912.08193.pdf.

kwargs

Parameter	Description
rpn_pre_nms_top_n_train	Optional int. Number of proposals to keep before applying NMS during training. Default: 2000
rpn_pre_nms_top_n_test	Optional int. Number of proposals to keep before applying NMS during testing. Default: 1000
rpn_post_nms_top_n_train	Optional int. Number of proposals to keep after applying NMS during training. Default: 2000
rpn_post_nms_top_n_test	Optional int. Number of proposals to keep after applying NMS during testing. Default: 1000
rpn_nms_thresh	Optional float. NMS threshold used for postprocessing the RPN proposals. Default: 0.7
rpn_fg_iou_thresh	Optional float. Minimum IoU between the anchor and the GT box so that they can be considered as positive during training of the RPN. Default: 0.7
rpn_bg_iou_thresh	Optional float. Maximum IoU between the anchor and the GT box so that they can be considered as negative during training of the RPN. Default: 0.3
rpn_batch_size_per_image	Optional int. Number of anchors that are sampled during training of the RPN for computing the loss. Default: 256
rpn_positive_fraction	Optional float. Proportion of positive anchors in a mini-batch during training of the RPN. Default: 0.5
box_score_thresh	Optional float. During inference, only return proposals with a classification score greater than box_score_thresh Default: 0.05
box_nms_thresh	Optional float. NMS threshold for the prediction head. Used during inference. Default: 0.5
box_detections_per_img	Optional int. Maximum number of detections per image, for all classes. Default: 100
box_fg_iou_thresh	Optional float. Minimum IoU between the proposals and the GT box so that they can be considered as positive during training of the classification head. Default: 0.5
box_bg_iou_thresh	Optional float. Maximum IoU between the proposals and the GT box so that they can be considered as negative during training of the classification head. Default: 0.5
box_batch_size_per_image	Optional int. Number of proposals that are sampled during training of the classification head. Default: 512
box_positive_fraction	Optional float. Proportion of positive proposals in a mini-batch during training of the classification head. Default: 0.25

Returns: MaskRCNN Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

average_precision_score(detect_thresh=0.5, iou_thresh=0.5, mean=False, show_progress=True, tta_prediction=False)

Computes average precision on the validation set for each class.

Returns: dict if mean is False otherwise float

static backbones(): Supported list of backbones for this model.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None, **kwargs)

Creates a MaskRCNN Instance segmentation object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: MaskRCNN Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict(image_path, threshold=0.5, nms_overlap=0.1, return_scores=True, visualize=False, resize=False, tta_prediction=False, **kwargs)

Predicts and displays the results of a trained model on a single image. This method is only supported for RGB images.

Parameter	Description
image_path	Required. Path to the image file to make the predictions on.
thresh	Optional float. The probability above which a detection will be considered valid.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.
return_scores	Optional boolean. Will return the probability scores of the bounding box predictions if True.
visualize	Optional boolean. Displays the image with predicted bounding boxes if True.
resize	Optional boolean. Resizes the image to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the image is resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the image (of the same size as the model was trained on).
tta_prediction	Optional bool. Perform test time augmentation while predicting

kwargs

Parameter	Description
batch_size	Optional int. Batch size to be used during tiled inferencing
min_obj_size	Optional int. Minimum object size to be detected.

Returns: ‘List’ of xmin, ymin, width, height, labels, scores, of predicted bounding boxes on the given image

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=4, mode='mask', mask_threshold=0.5, box_threshold=0.7, tta_prediction=False, imsize=5, index=0, alpha=0.5, cmap='tab20', **kwargs)

Displays the results of a trained model on a part of the validation set.

Parameter	Description

rows	Optional int. Number of rows of results to be displayed.
mode	Required arguments within [‘bbox’, ‘mask’, ‘bbox_mask’]. `bbox` - For visualizing only bounding boxes. `mask` - For visualizing only mask `bbox_mask` - For visualizing both mask and bounding boxes.
mask_threshold	Optional float. The probability above which a pixel will be considered mask.
box_threshold	Optional float. The probability above which a detection will be considered valid.
tta_prediction	Optional bool. Perform test time augmentation while predicting

property supported_backbones: Supported list of backbones for this model.

property supported_datasets: Supported dataset types for this model.

static transformer_backbones()

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

MMDetection

class arcgis.learn.MMDetection(data, model, model_weight=False, pretrained_path=None, **kwargs)

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
model	Required model name or path to the configuration file from `MMDetection` repository. The list of the supported models can be queried using `supported_models` .
model_weight	Optional path of the model weight from `MMDetection` repository.
pretrained_path	Optional string. Path where pre-trained model is saved.

Returns: MMDetection Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

average_precision_score(detect_thresh=0.2, iou_thresh=0.1, mean=False, show_progress=True)

Computes average precision on the validation set for each class.

Parameter	Description
detect_thresh	Optional float. The probability above which a detection will be considered for computing average precision.
iou_thresh	Optional float. The intersection over union threshold with the ground truth labels, above which a predicted bounding box will be considered a true positive.
mean	Optional bool. If False returns class-wise average precision otherwise returns mean average precision.

Returns: dict if mean is False otherwise float

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a MMDetection object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: MMDetection Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict(image_path, threshold=0.5, nms_overlap=0.1, return_scores=False, visualize=False, resize=False)

Runs prediction on an Image. This method is only supported for RGB images.

Parameter	Description
image_path	Required. Path to the image file to make the predictions on.
threshold	Optional float. The probability above which a detection will be considered valid.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.
return_scores	Optional boolean. Will return the probability scores of the bounding box predictions if True.
visualize	Optional boolean. Displays the image with predicted bounding boxes if True.
resize	Optional boolean. Resizes the image to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the image is resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the image (of the same size as the model was trained on).

Returns: Returns a tuple with predictions, labels and optionally confidence scores if return_scores=True. The predicted bounding boxes are returned as a list of lists containing the xmin, ymin, width and height of each predicted object in each image. The labels are returned as a list of class values and the confidence scores are returned as a list of floats indicating the confidence of each prediction.

Runs prediction on a video and appends the output VMTI predictions in the metadata file. This method is only supported for RGB images.

Parameter	Description
input_video_path	Required. Path to the video file to make the predictions on.
metadata_file	Required. Path to the metadata csv file where the predictions will be saved in VMTI format.
threshold	Optional float. The probability above which a detection will be considered.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.
track	Optional bool. Set this parameter as True to enable object tracking.
visualize	Optional boolean. If True a video is saved with prediction results.
output_file_path	Optional path. Path of the final video to be saved. If not supplied, video will be saved at path input_video_path appended with _prediction.
multiplex	Optional boolean. Runs Multiplex using the VMTI detections.
multiplex_file_path	Optional path. Path of the multiplexed video to be saved. By default a new file with _multiplex.MOV extension is saved in the same folder.
tracking_options	Optional dictionary. Set different parameters for object tracking. assignment_iou_thrd parameter is used to assign threshold for assignment of trackers, vanish_frames is the number of frames the object should be absent to consider it as vanished, detect_frames is the number of frames an object should be detected to track it.
visual_options	Optional dictionary. Set different parameters for visualization. show_scores boolean, to view scores on predictions, show_labels boolean, to view labels on predictions, thickness integer, to set the thickness level of box, fontface integer, fontface value from opencv values, color tuple (B, G, R), tuple containing values between 0-255.
resize	Optional boolean. Resizes the video frames to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the video frames are resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the frame (of the same size as the model was trained on).

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, thresh=0.5, nms_overlap=0.1)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.
thresh	Optional float. The probability above which a detection will be considered valid.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.

property supported_datasets: Supported dataset types for this model.

supported_models = ['atss', 'carafe', 'cascade_rcnn', 'cascade_rpn', 'dcn', 'detectors', 'double_heads', 'dynamic_rcnn', 'empirical_attention', 'fcos', 'foveabox', 'fsaf', 'ghm', 'hrnet', 'libra_rcnn', 'nas_fcos', 'pafpn', 'pisa', 'regnet', 'reppoints', 'res2net', 'sabl', 'vfnet']: List of models supported by this class.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

DETReg

class arcgis.learn.DETReg(data, backbone='resnet50', pretrained_path=None, **kwargs)

Model architecture from https://arxiv.org/abs/2106.04550. Creates a DETReg object detection model, based on https://github.com/amirbar/DETReg.

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
backbone	Optional string. Backbone convolutional neural network model used for feature extraction. resnet50 is the only backbone that is currently supported. resnet50 is used by default.
pretrained_path	Optional string. Path where pre-trained model is saved.

Returns: DETReg Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

average_precision_score(detect_thresh=0.2, iou_thresh=0.1, mean=False, show_progress=True)

Computes average precision on the validation set for each class.

Parameter	Description
detect_thresh	Optional float. The probability above which a detection will be considered for computing average precision.
iou_thresh	Optional float. The intersection over union threshold with the ground truth labels, above which a predicted bounding box will be considered a true positive.
mean	Optional bool. If False returns class-wise average precision otherwise returns mean average precision.

Returns: dict if mean is False otherwise float

static backbones(): Supported list of backbones for this model.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a DETReg object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: DETReg Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict(image_path, threshold=0.5, nms_overlap=0.1, return_scores=False, visualize=False, resize=False)

Runs prediction on an Image. This method is only supported for RGB images.

Parameter	Description
image_path	Required. Path to the image file to make the predictions on.
threshold	Optional float. The probability above which a detection will be considered valid.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.
return_scores	Optional boolean. Will return the probability scores of the bounding box predictions if True.
visualize	Optional boolean. Displays the image with predicted bounding boxes if True.
resize	Optional boolean. Resizes the image to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the image is resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the image (of the same size as the model was trained on).

Returns: Returns a tuple with predictions, labels and optionally confidence scores if return_scores=True. The predicted bounding boxes are returned as a list of lists containing the xmin, ymin, width and height of each predicted object in each image. The labels are returned as a list of class values and the confidence scores are returned as a list of floats indicating the confidence of each prediction.

Runs prediction on a video and appends the output VMTI predictions in the metadata file. This method is only supported for RGB images.

Parameter	Description
input_video_path	Required. Path to the video file to make the predictions on.
metadata_file	Required. Path to the metadata csv file where the predictions will be saved in VMTI format.
threshold	Optional float. The probability above which a detection will be considered.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.
track	Optional bool. Set this parameter as True to enable object tracking.
visualize	Optional boolean. If True a video is saved with prediction results.
output_file_path	Optional path. Path of the final video to be saved. If not supplied, video will be saved at path input_video_path appended with _prediction.
multiplex	Optional boolean. Runs Multiplex using the VMTI detections.
multiplex_file_path	Optional path. Path of the multiplexed video to be saved. By default a new file with _multiplex.MOV extension is saved in the same folder.
tracking_options	Optional dictionary. Set different parameters for object tracking. assignment_iou_thrd parameter is used to assign threshold for assignment of trackers, vanish_frames is the number of frames the object should be absent to consider it as vanished, detect_frames is the number of frames an object should be detected to track it.
visual_options	Optional dictionary. Set different parameters for visualization. show_scores boolean, to view scores on predictions, show_labels boolean, to view labels on predictions, thickness integer, to set the thickness level of box, fontface integer, fontface value from opencv values, color tuple (B, G, R), tuple containing values between 0-255.
resize	Optional boolean. Resizes the video frames to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the video frames are resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the frame (of the same size as the model was trained on).

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, thresh=0.5, nms_overlap=0.1)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.
thresh	Optional float. The probability above which a detection will be considered valid.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.

property supported_backbones: Supported list of backbones for this model.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

EfficientDet

class arcgis.learn.EfficientDet(data, backbone=None, pretrained_path=None, *args, **kwargs)

Creates a EfficientDet model for Object Detection. Supports RGB -JPEG imagery. Based on TFLite Model Maker

Argument	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function. Only (JPEG+PASCAL_VOC_rectangles) format supported.
backbone	Optional String. Backbone convolutional neural network model used for EfficientDet, which is efficientdet_lite0 by default.
pretrained_path	Optional String. Path where a compatible pre-trained model is saved. Accepts a Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Returns: EfficientDet Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

average_precision_score(mean=False)

Computes average precision on the validation set for each class.

Argument	Description
mean	Optional bool. If False returns class-wise average precision otherwise returns mean average precision.

Returns: dict if mean is False otherwise float

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.Recommended to set to False.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a EfficientDet object from an Esri Model Definition (EMD) file.

Argument	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: EfficientDet Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict(image_path, threshold=0.5, nms_overlap=0.1, return_scores=True, visualize=False, resize=False, **kwargs)

Predicts and displays the results of a trained model on a single image. This method is only supported for RGB images.

Argument	Description
image_path	Required. Path to the image file to make the predictions on.
thresh	Optional float. The probability above which a detection will be considered valid.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.
return_scores	Optional boolean. Will return the probability scores of the bounding box predictions if True.
visualize	Optional boolean. Displays the image with predicted bounding boxes if True.
resize	Optional boolean. Resizes the image to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the image is resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the image (of the same size as the model was trained on).

Returns: ‘List’ of xmin, ymin, width, height, labels, scores, of predicted bounding boxes on the given image

Runs prediction on a video and appends the output VMTI predictions in the metadata file. This method is only supported for RGB images.

Argument	Description
input_video_path	Required. Path to the video file to make the predictions on.
metadata_file	Required. Path to the metadata csv file where the predictions will be saved in VMTI format.
threshold	Optional float. The probability above which a detection will be considered.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.
track	Optional bool. Set this parameter as True to enable object tracking.
visualize	Optional boolean. If True a video is saved with prediction results.
output_file_path	Optional path. Path of the final video to be saved. If not supplied, video will be saved at path input_video_path appended with _prediction.
multiplex	Optional boolean. Runs Multiplex using the VMTI detections.
multiplex_file_path	Optional path. Path of the multiplexed video to be saved. By default a new file with _multiplex.MOV extension is saved in the same folder.
tracking_options	Optional dictionary. Set different parameters for object tracking. assignment_iou_thrd parameter is used to assign threshold for assignment of trackers, vanish_frames is the number of frames the object should be absent to consider it as vanished, detect_frames is the number of frames an object should be detected to track it.
visual_options	Optional dictionary. Set different parameters for visualization. show_scores boolean, to view scores on predictions, show_labels boolean, to view labels on predictions, thickness integer, to set the thickness level of box, fontface integer, fontface value from opencv values, color tuple (B, G, R), tuple containing values between 0-255.
resize	Optional boolean. Resizes the image to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the image is resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the image (of the same size as the model was trained on).

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, thresh=0.5, nms_overlap=0.1)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.
thresh	Optional float. The probability above which a detection will be considered valid.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.

property supported_backbones: Supported torchvision backbones for this model.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

Pixel Classification Models

UnetClassifier

class arcgis.learn.UnetClassifier(data, backbone=None, pretrained_path=None, backend='pytorch', *args, **kwargs)

Creates a Unet like classifier based on given pretrained encoder.

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
backbone	Optional string. Backbone convolutional neural network model used for feature extraction, which is resnet34 by default. Supported backbones: ResNet family and specified Timm models(experimental support) from `backbones()`.
pretrained_path	Optional string. Path where pre-trained model is saved.
backend	Optional string. Controls the backend framework to be used for this model, which is ‘pytorch’ by default. valid options are ‘pytorch’, ‘tensorflow’

kwargs

Parameter	Description
class_balancing	Optional boolean. If True, it will balance the cross-entropy loss inverse to the frequency of pixels per class. Default: False.
mixup	Optional boolean. If True, it will use mixup augmentation and mixup loss. Default: False
focal_loss	Optional boolean. If True, it will use focal loss Default: False
dice_loss_fraction	Optional float. Min_val=0, Max_val=1 If > 0 , model will use a combination of default or focal(if focal=True) loss with the specified fraction of dice loss. E.g. for dice = 0.3, loss = (1-0.3)default loss + 0.3dice Default: 0
dice_loss_average	Optional str. micro: Micro dice coefficient will be used for loss calculation. macro: Macro dice coefficient will be used for loss calculation. A macro-average will compute the metric independently for each class and then take the average (hence treating all classes equally), whereas a micro-average will aggregate the contributions of all classes to compute the average metric. In a multi-class classification setup, micro-average is preferable if you suspect there might be class imbalance (i.e you may have many more examples of one class than of other classes) Default: ‘micro’
ignore_classes	Optional list. It will contain the list of class values on which model will not incur loss. Default: []

Returns: UnetClassifier Object

accuracy(): Computes per pixel accuracy on validation set.

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

static backbones(): Supported list of backbones for this model.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_emd(data, emd_path)

Creates a Unet like classifier from an Esri Model Definition (EMD) file.

Parameter	Description
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.
emd_path	Required string. Path to Esri Model Definition file.

Returns: UnetClassifier Object

classmethod from_model(emd_path, data=None)

Creates a Unet like classifier from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: UnetClassifier Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

mIOU(mean=False, show_progress=True)

Computes mean IOU on the validation set for each class.

Parameter	Description
mean	Optional bool. If False returns class-wise mean IOU, otherwise returns mean iou of all classes combined.
show_progress	Optional bool. Displays the progress bar if True.

Returns: dict if mean is False otherwise float

per_class_metrics(ignore_classes=[])

Computer per class precision, recall and f1-score on validation set.

Parameter	Description
self	segmentation model object -> [PSPNetClassifier \| UnetClassifier \| DeepLab]
ignore_classes	Optional list. It will contain the list of class values on which model will not incur loss. Default: []

Returns per class precision, recall and f1 scores

plot_losses(): Plot validation and training losses after fitting the model.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, **kwargs)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.

property supported_backbones: Supported list of backbones for this model.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

PSPNetClassifier

class arcgis.learn.PSPNetClassifier(data, backbone=None, use_unet=True, pyramid_sizes=[1, 2, 3, 6], pretrained_path=None, unet_aux_loss=False, pointrend=False, *args, **kwargs)

Model architecture from https://arxiv.org/abs/1612.01105. Creates a PSPNet Image Segmentation/ Pixel Classification model.

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
backbone	Optional string. Backbone convolutional neural network model used for feature extraction, which is resnet50 by default. Supported backbones: ResNet, DenseNet, VGG families and specified Timm models(experimental support) from `backbones()`.
use_unet	Optional Bool. Specify whether to use Unet-Decoder or not, Default True.
pyramid_sizes	Optional List. The sizes at which the feature map is pooled at. Currently set to the best set reported in the paper, i.e, (1, 2, 3, 6)
pretrained	Optional Bool. If True, use the pretrained backbone
pretrained_path	Optional string. Path where pre-trained PSPNet model is saved.
unet_aux_loss	Optional. Bool If True will use auxiliary loss for PSUnet. Default set to False. This flag is applicable only when use_unet is True.
pointrend	Optional boolean. If True, it will use PointRend architecture on top of the segmentation head. Default: False. PointRend architecture from https://arxiv.org/pdf/1912.08193.pdf.

kwargs

Parameter	Description
class_balancing	Optional boolean. If True, it will balance the cross-entropy loss inverse to the frequency of pixels per class. Default: False.
mixup	Optional boolean. If True, it will use mixup augmentation and mixup loss. Default: False
focal_loss	Optional boolean. If True, it will use focal loss. Default: False
dice_loss_fraction	Optional float. Min_val=0, Max_val=1 If > 0 , model will use a combination of default or focal(if focal=True) loss with the specified fraction of dice loss. Example: for dice = 0.3, loss = (1-0.3)default loss + 0.3dice Default: 0
dice_loss_average	Optional str. “`micro`”: Micro dice coefficient will be used for loss calculation. “`macro`”: Macro dice coefficient will be used for loss calculation. A macro-average will compute the metric independently for each class and then take the average (hence treating all classes equally), whereas a micro-average will aggregate the contributions of all classes to compute the average metric. In a multi-class classification setup, micro-average is preferable if you suspect there might be class imbalance (i.e you may have many more examples of one class than of other classes) Default: ‘micro’
ignore_classes	Optional list. It will contain the list of class values on which model will not incur loss. Default: []
keep_dilation	Optional boolean. When PointRend architecture is used, keep_dilation=True can potentially improve accuracy at the cost of memory consumption. Default: False

Returns: PSPNetClassifier Object

accuracy(input=None, target=None, void_code=0, class_mapping=None): Computes per pixel accuracy.

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

static backbones(): Supported list of backbones for this model.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

freeze(): Freezes the pretrained backbone.

classmethod from_model(emd_path, data=None)

Creates a PSPNet classifier from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: PSPNetClassifier Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

mIOU(mean=False, show_progress=True)

Computes mean IOU on the validation set for each class.

Parameter	Description
mean	Optional bool. If False returns class-wise mean IOU, otherwise returns mean iou of all classes combined.
show_progress	Optional bool. Displays the progress bar if True.

Returns: dict if mean is False otherwise float

per_class_metrics(ignore_classes=[])

Computer per class precision, recall and f1-score on validation set.

Parameter	Description
self	segmentation model object -> [PSPNetClassifier \| UnetClassifier \| DeepLab]
ignore_classes	Optional list. It will contain the list of class values on which model will not incur loss. Default: []

Returns per class precision, recall and f1 scores

plot_losses(): Plot validation and training losses after fitting the model.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, **kwargs)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.

property supported_backbones: Supported list of backbones for this model.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

DeepLab

class arcgis.learn.DeepLab(data, backbone=None, pretrained_path=None, pointrend=False, *args, **kwargs)

Model architecture from https://arxiv.org/abs/1706.05587. Creates a DeepLab Image Segmentation/ Pixel Classification model, based on https://github.com/pytorch/vision/tree/master/torchvision/models/segmentation.

Parameter	Description
data	Required fastai Databunch. Returned data object from function.
backbone	Optional string. Backbone convolutional neural network model used for feature extraction, which is resnet101 by default since it is pretrained in torchvision. Supported backbones: ResNet, DenseNet, VGG family and specified Timm models(experimental support) from `backbones()`.
pretrained_path	Optional string. Path where pre-trained model is saved.
pointrend	Optional boolean. If True, it will use PointRend architecture on top of the segmentation head. Default: False. PointRend architecture from https://arxiv.org/pdf/1912.08193.pdf.

kwargs

Parameter	Description
class_balancing	Optional boolean. If True, it will balance the cross-entropy loss inverse to the frequency of pixels per class. Default: False.
mixup	Optional boolean. If True, it will use mixup augmentation and mixup loss. Default: False
focal_loss	Optional boolean. If True, it will use focal loss. Default: False
dice_loss_fraction	Optional float. Min_val=0, Max_val=1 If > 0 , model will use a combination of default or focal(if focal=True) loss with the specified fraction of dice loss. E.g. for dice = 0.3, loss = (1-0.3)default loss + 0.3dice Default: 0
dice_loss_average	Optional str. micro: Micro dice coefficient will be used for loss calculation. macro: Macro dice coefficient will be used for loss calculation. A macro-average will compute the metric independently for each class and then take the average (hence treating all classes equally), whereas a micro-average will aggregate the contributions of all classes to compute the average metric. In a multi-class classification setup, micro-average is preferable if you suspect there might be class imbalance (i.e you may have many more examples of one class than of other classes) Default: ‘micro’
ignore_classes	Optional list. It will contain the list of class values on which model will not incur loss. Default: []
keep_dilation	Optional boolean. When PointRend architecture is used, keep_dilation=True can potentially improves accuracy at the cost of memory consumption. Default: False

Returns: DeepLab Object

accuracy(): Computes per pixel accuracy on validation set.

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

static backbones(): Supported list of backbones for this model.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a DeepLab semantic segmentation object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: DeepLab Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

mIOU(mean=False, show_progress=True)

Computes mean IOU on the validation set for each class.

Parameter	Description
mean	Optional bool. If False returns class-wise mean IOU, otherwise returns mean iou of all classes combined.
show_progress	Optional bool. Displays the progress bar if True.

Returns: dict if mean is False otherwise float

per_class_metrics(ignore_classes=[])

Computer per class precision, recall and f1-score on validation set.

Parameter	Description
self	segmentation model object -> [PSPNetClassifier \| UnetClassifier \| DeepLab]
ignore_classes	Optional list. It will contain the list of class values on which model will not incur loss. Default: []

Returns per class precision, recall and f1 scores

plot_losses(): Plot validation and training losses after fitting the model.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, **kwargs)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.

property supported_backbones: Supported list of backbones for this model.

property supported_datasets: Supported dataset types for this model.

static transformer_backbones()

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

BDCNEdgeDetector

class arcgis.learn.BDCNEdgeDetector(data, backbone='vgg19', pretrained_path=None)

Model architecture from https://arxiv.org/pdf/1902.10903.pdf. Creates a BDCNEdgeDetector model

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
backbone	Optional string. Backbone convolutional neural network model used for feature extraction, which is vgg19 by default. Supported backbones: ResNet, Vgg family and specified Timm models(experimental support) from `backbones()`.
pretrained_path	Optional string. Path where pre-trained model is saved.

Returns: BDCNEdgeDetector Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

static backbones(): Supported list of backbones for this model.

compute_precision_recall(thresh=0.5, buffer=3, show_progress=True)

Computes precision, recall and f1 score on validation set.

Parameter	Description
thresh	Optional float. The probability on which the detection will be considered edge pixel.
buffer	Optional int. pixels in neighborhood to consider true detection.

Returns: dict

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a BDCNEdgeDetector object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: BDCNEdgeDetector Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, thresh=0.5, thinning=True, **kwargs): Displays the results of a trained model on a part of the validation set.

property supported_backbones: Supported list of backbones for this model.

property supported_datasets: Supported dataset types for this model.

static transformer_backbones()

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

HEDEdgeDetector

class arcgis.learn.HEDEdgeDetector(data, backbone='vgg19', pretrained_path=None, **kwargs)

Model architecture from https://arxiv.org/pdf/1504.06375.pdf. Creates a HEDEdgeDetector model

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
backbone	Optional string. Backbone convolutional neural network model used for feature extraction, which is vgg19 by default. Supported backbones: ResNet, Vgg family and specified Timm models(experimental support) from `backbones()`.
pretrained_path	Optional string. Path where pre-trained model is saved.

Returns: HEDEdgeDetector Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

static backbones(): Supported list of backbones for this model.

compute_precision_recall(thresh=0.5, buffer=3, show_progress=True)

Computes precision, recall and f1 score on validation set.

Parameter	Description
thresh	Optional float. The probability on which the detection will be considered edge pixel.
buffer	Optional int. pixels in neighborhood to consider true detection.

Returns: dict

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a HEDEdgeDetector object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: HEDEdgeDetector Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, thresh=0.5, thinning=True, **kwargs): Displays the results of a trained model on a part of the validation set.

property supported_backbones: Supported list of backbones for this model.

property supported_datasets: Supported dataset types for this model.

static transformer_backbones()

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

MultiTaskRoadExtractor

class arcgis.learn.MultiTaskRoadExtractor(data, backbone=None, pretrained_path=None, *args, **kwargs)

Creates a Multi-Task Learning model for binary segmentation of roads. Supports RGB and Multispectral Imagery. Implementation based on https://doi.org/10.1109/CVPR.2019.01063 .

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
backbone	Optional String. Backbone convolutional neural network model used for feature extraction. If hourglass is chosen as the mtl_model (Architecture), then this parameter is ignored as hourglass uses a special customised architecture. This parameter is used with linknet model. Default: ‘resnet34’ Supported backbones: ResNet family and specified Timm models(experimental support) from `backbones()`.
pretrained_path	Optional String. Path where a compatible pre-trained model is saved. Accepts a Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

kwargs

Parameter	Description
mtl_model	Optional String. It is used to create model from linknet or hourglass based neural architectures. Supported: ‘linknet’, ‘hourglass’. Default: ‘hourglass’
gaussian_thresh	Optional float. Sets the gaussian threshold which allows to set the required road width. Range: 0.0 to 1.0 Default: 0.76
orient_bin_size	Optional Int. Sets the bin size for orientation angles. Default: 20
orient_theta	Optional Int. Sets the width of orientation mask. Default: 8

Returns: MultiTaskRoadExtractor Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

static backbones(): Supported list of backbones for this model.

fit(epochs=10, lr=None, **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a Multi-Task Learning model for binary segmentation from a Deep Learning Package(DLPK) or Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: MultiTaskRoadExtractor Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

mIOU(mean=False, show_progress=True)

Computes mean IOU on the validation set for each class.

Parameter	Description
mean	Optional bool. If False returns class-wise mean IOU, otherwise returns mean iou of all classes combined.
show_progress	Optional bool. Displays the prgress bar if True.

Returns: dict if mean is False otherwise float

plot_losses(): Plot validation and training losses after fitting the model.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=2, **kwargs)

Shows the ground truth and predictions of model side by side.

kwargs

Parameter	Description
rows	Number of rows of data to be displayed, if batch size is smaller, then the rows will display the value provided for batch size.
alpha	Optional Float. Opacity parameter for label overlay on image. Float [0.0 - 1.0] Default: 0.6

property supported_backbones: Supported list of backbones for this model.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

ConnectNet

class arcgis.learn.ConnectNet(data, backbone=None, pretrained_path=None, *args, **kwargs)

Creates a ConnectNet model for binary segmentation of linear features. Supports RGB and Multispectral Imagery. Implementation based on https://doi.org/10.1109/CVPR.2019.01063 .

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
backbone	Optional String. Backbone CNN model to be used for creating the base. If hourglass is chosen as the mtl_model (Architecture), then this parameter is ignored as hourglass uses a special customised architecture. This parameter is to be used with linknet architecture. Default: ‘resnet34’ Use supported_backbones property to get the list of all the supported backbones.
pretrained_path	Optional String. Path where a compatible pre-trained model is saved. Accepts a Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

kwargs

Parameter	Description
mtl_model	Optional String. It is used to create model from linknet or hourglass based neural architectures. Supported: ‘linknet’, ‘hourglass’. Default: ‘hourglass’
gaussian_thresh	Optional float. Sets the gaussian threshold which allows to set the required width of the linear feature. Range: 0.0 to 1.0 Default: 0.76
orient_bin_size	Optional Int. Sets the bin size for orientation angles. Default: 20
orient_theta	Optional Int. Sets the width of orientation mask. Default: 8

Returns: ConnectNet Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

static backbones(): Supported list of backbones for this model.

fit(epochs=10, lr=None, **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a Multi-Task Learning model for binary segmentation from a Deep Learning Package(DLPK) or Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: MultiTaskRoadExtractor Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

mIOU(mean=False, show_progress=True)

Computes mean IOU on the validation set for each class.

Parameter	Description
mean	Optional bool. If False returns class-wise mean IOU, otherwise returns mean iou of all classes combined.
show_progress	Optional bool. Displays the prgress bar if True.

Returns: dict if mean is False otherwise float

plot_losses(): Plot validation and training losses after fitting the model.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=2, **kwargs)

Shows the ground truth and predictions of model side by side.

kwargs

Parameter	Description
rows	Number of rows of data to be displayed, if batch size is smaller, then the rows will display the value provided for batch size.
alpha	Optional Float. Opacity parameter for label overlay on image. Float [0.0 - 1.0] Default: 0.6

property supported_backbones: Supported list of backbones for this model.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

ChangeDetector

class arcgis.learn.ChangeDetector(data, backbone=None, attention_type='PAM', pretrained_path=None, **kwargs)

Creates a Change Detection model.

A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection - https://www.mdpi.com/2072-4292/12/10/1662

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
backbone	Optional function. Backbone CNN model to be used for creating the encoder of the `ConnectNet`, which is resnet18 by default. It supports the ResNet family of backbones.
attention_type	Optional string. It’s value can be either be “PAM” (Pyramid Attention Module) or “BAM” (Basic Attention Module). Defaults to “PAM”.
pretrained_path	Optional string. Path where pre-trained model is saved.

Returns: ConnectNet object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a ChangeDetector model from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Optional fastai Databunch. Returned data object from `prepare_data()` function or None for inferencing.

Returns: ConnectNet Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

precision_recall_score(): Computes precision, recall and f1 score.

predict(before_image, after_image, **kwargs)

Predict on a pair of images.

Parameter	Description
before_image	Required string. Path to image from before.
after_image	Required string. Path to image from later.

Kwargs

Parameter	Description
crop_predict	Optional Boolean. If True, It will predict using a sliding window strategy. Typically, used when image size is larger than the chip_size the model is trained on. Default False.
visualize	Optional Boolean. If True, It will plot the predictions on the notebook. Default False.
save	Optional Boolean. If true will write the prediction file on the disk. Default False.

Returns: PyTorch Tensor of the change mask.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=4, **kwargs): Displays the results of a trained model on the validation set.

property supported_backbones: Supported torchvision backbones for this model.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

MMSegmentation

class arcgis.learn.MMSegmentation(data, model, model_weight=False, pretrained_path=None, **kwargs)

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
model	Required model name or path to the configuration file from `MMSegmentation` repository. The list of the supported models can be queried using `supported_models`
model_weight	Optional path of the model weight from `MMSegmentation` repository.
pretrained_path	Optional string. Path where pre-trained model is saved.

kwargs

class_balancing	Optional boolean. If True, it will balance the cross-entropy loss inverse to the frequency of pixels per class. Default: False.
ignore_classes	Optional list. It will contain the list of class values on which model will not incur loss. Default: []
seq_len	Optional int. Number of timestamp bands. Applicable for prithvi100m model only. Default: 1

Returns: MMSegmentation Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a MMSegmentation object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: MMSegmentation Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, thresh=0.5, thinning=True, **kwargs): Displays the results of a trained model on a part of the validation set.

property supported_datasets: Supported dataset types for this model.

supported_models = ['ann', 'apcnet', 'ccnet', 'cgnet', 'deeplabv3', 'deeplabv3plus', 'dmnet', 'dnlnet', 'emanet', 'fastscnn', 'fcn', 'gcnet', 'hrnet', 'mask2former', 'mobilenet_v2', 'nonlocal_net', 'ocrnet', 'psanet', 'pspnet', 'resnest', 'sem_fpn', 'unet', 'upernet']: List of models supported by this class.

supported_transformer_models = ['mask2former', 'prithvi100m']: List of transformer based models supported by this class.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

MaXDeepLab

class arcgis.learn.MaXDeepLab(data, backbone=None, pretrained_path=None, **kwargs)

Creates a MaXDeepLab panoptic segmentation model.

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function. MaXDeepLab only supports image sizes in multiples of 16 (e.g. 256, 416, etc.).
pretrained_path	Optional string. Path where pre-trained model is saved.

Returns: MaXDeepLab Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

compute_n_masks(): Computes the maximum number of class labels and masks in any chip in the entire dataset. Note: It might take long time for larger datasets.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a MaXDeepLab Panoptic Segmentation object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: MaXDeepLab Panoptic Segmentation Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

property supported_backbones: Supported backbones for this model.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

SamLoRA

class arcgis.learn.SamLoRA(data, backbone='vit_b', pretrained_path=None, **kwargs)

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
pretrained_path	Optional string. Path where pre-trained model is saved.

kwargs

class_balancing	Optional boolean. If True, it will balance the cross-entropy loss inverse to the frequency of pixels per class. Default: False.
ignore_classes	Optional list. It will contain the list of class values on which model will not incur loss. Default: []

Returns: SamLoRA Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a SamLoRA object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: SamLoRA Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, **kwargs)

Displays the results of a trained model on a part of the validation set. ===================== =========================================== Parameter Description ——————— ——————————————- rows Optional Integer. Number of rows of results

to be displayed.

kwargs
Parameter	Description
alpha	Optional Float. Default value is 0.5. Opacity of the lables for the corresponding images. Values range between 0 and 1, where 1 means opaque.

property supported_backbones: Supported list of backbones for this model.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

ClimaX

class arcgis.learn.ClimaX(data, backbone='5.625deg', pretrained_path=None, *args, **kwargs)

Creates ClimaX model object: a foundational model for weather and climate forecasting tasks.

Parameter	Description
data	Required fastai Databunch. Returned data object from prepare_data function.
backbone	Optional string. pretrained foundational models as backbone. Compatible backbones: ‘5.625deg’, ‘1.40625deg’. Default set to ‘5.625deg’.
pretrained_path	Optional string. Path where pre-trained model is saved.

Keyword Arguments

Parameter	Description
patch_size	Optional int. Patch size for generating patch embeddings. Default: 2
embed_dim	Optional int. Dimension of embeddings. Default: 1024
depth	Optional int. Depth of model. Default: 8
num_heads	Optional int. Number of attention heads. Default: 16
mlp_ratio	Optional float. Ratio of MLP. Default: 4.0
decoder_depth	Optional int. Depth of decoder. Default: 2
drop_path	Optional int. stochastic depth or randomly drops entire layers. Default: 0.1
drop_rate	Optional int. randomly drops neurons. Default: 0.1
parallel_patch_embed	Optional bol. parallel embdedding of patches. Default: True

Returns: ClimaX Object

accuracy(): Computes latitude weighted accuracy on validation set.

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

compute_metrics(): Computes latitude weighted root mean squared error on validation set.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a ClimaX object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from prepare_data function or None for inferencing.

Returns: ClimaX Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, variable='', **kwargs)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.
total_sample_size	Optional int. Number of rows of results to be displayed.

variable_no Optional int. variable count to be displayed ===================== =========================================== kwargs

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

Image Translation Models

CycleGAN

class arcgis.learn.CycleGAN(data, pretrained_path=None, gen_blocks=9, lsgan=True, *args, **kwargs)

Creates a model object which generates images of type A from type B or type B from type A.

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
pretrained_path	Optional string. Path where pre-trained model is saved.
gen_blocks	Optional integer. Number of ResNet blocks to use in generator.
lsgan	Optional boolean. If True, it will use Mean Squared Error else it will use Binary Cross Entropy.

Returns: CycleGAN Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

compute_metrics(): Computes Frechet Inception Distance (FID) on validation set.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a CycleGAN object from an Esri Model Definition (EMD) file.

Parameter	Description
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Returns: CycleGAN Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict(img_path, convert_to)

Predicts and display the image.

Parameter	Description
img_path	Required path of an image.
convert_to	‘A’ if we want to generate image of type ‘A’ from type ‘B’ or ‘B’ if we want to generate image of type ‘B’ from type ‘A’ where A and B are the domain specifications that were used while training.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5, **kwargs)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.

kwargs

rgb_bands

Optional list of integers (band numbers) to be considered for rgb visualization.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

Pix2Pix

class arcgis.learn.Pix2Pix(data, pretrained_path=None, backbone=None, perceptual_loss=False, *args, **kwargs)

Creates a model object which generates fake images of type B from type A.

Parameter	Description
data	Required fastai Databunch with image chip sizes in multiples of 256. Returned data object from `prepare_data()` function.
pretrained_path	Optional string. Path where pre-trained model is saved.
backbone	Optional function. Backbone CNN model to be used for creating the base of the `Pix2Pix`, which is UNet with vanilla encoder by default. Compatible backbones as encoder: ‘resnet18’, ‘resnet34’, ‘resnet50’, “resnet101”, “resnet152”, ‘resnext50’, ‘wide_resnet50’
perceptual_loss	Optional boolean. True when Perceptual loss is used. Default set to False.

Returns: Pix2Pix Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

compute_metrics(show_progress=True): Computes Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) on validation set. Additionally, computes Frechet Inception Distance (FID) for RGB imagery only.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a Pix2Pix object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: Pix2Pix Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict(path)

Predicts and display the image.

Parameter	Description
img_path	Required path of an image.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=2, **kwargs)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.

kwargs

rgb_bands

Optional list of integers (band numbers) to be considered for rgb visualization.

property supported_backbones: Supported backbones for this model.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

Pix2PixHD

class arcgis.learn.Pix2PixHD(data, pretrained_path=None, *args, **kwargs)

Creates a model object which generates fake images of type B from type A.

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
pretrained_path	Optional string. Path where pre-trained model is saved.

kwargs

n_gen_filters	Optional int. Number of gen filters in first conv layer. Default: 64
gen_network	Optional string (global/local). Selects model to use for generator. Use global if gpu memory is less. Default: “local”
n_downsample_global	Optional int. Number of downsampling layers in gen_network Default: 4
n_blocks_global	Optional int. Number of residual blocks in the global generator network. Default: 9
n_local_enhancers	Optional int. Number of local enhancers to use. Default: 1
n_blocks_local	Optional int. number of residual blocks in the local enhancer network. Default: 3
norm	Optional string. instance normalization or batch normalization Default: “instance”
lsgan	Optional bool. Use least square GAN, if True, use vanilla GAN. Default: True
n_dscr_filters	Optional int. number of discriminator filters in first conv layer. Default: 64
n_layers_dscr	Optional int. only used if which_model_net_dscr==n_layers. Default: 3
n_dscr	Optional int. number of discriminators to use. Default: 2
feat_loss	Optional bool. if ‘True’, use discriminator feature matching loss. Default: True
vgg_loss	Optional bool. if ‘True’, use VGG feature matching loss. Default: True (supported for 3 band imagery only).
lambda_feat	Optional int. weight for feature matching loss. Default: 10
lambda_l1	Optional int. weight for feature matching loss. Default: 100 (not supported for 3 band imagery)

Returns: Pix2PixHD Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

compute_metrics(accuracy=True, show_progress=True): Computes Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) on validation set. Additionally, computes Frechet Inception Distance (FID) for RGB imagery only.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a Pix2PixHD object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: Pix2PixHD Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict(path)

Predicts and display the image.

Parameter	Description
img_path	Required path of an image.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=2, **kwargs)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.

kwargs

rgb_bands

Optional list of integers (band numbers) to be considered for rgb visualization.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

WNet_cGAN

class arcgis.learn.WNet_cGAN(data, pretrained_path=None, *args, **kwargs)

Creates a model object which generates images of type C from type A and type B.

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
pretrained_path	Optional string. Path where pre-trained model is saved.

Returns: WNet_cGAN Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

compute_metrics(accuracy=True, show_progress=True): Computes Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) on validation set.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a WNet_cGAN object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: WNet_cGAN Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict(img_path1, img_path2)

Predicts and display the image. This method is only supported for RGB images.

Parameter	Description
img_path1	Required path of an image 1.
img_path2	Required path of an image 2.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=2, **kwargs)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

SuperResolution

class arcgis.learn.SuperResolution(data, backbone=None, pretrained_path=None, *args, **kwargs)

Creates a model object which increases the resolution and improves the quality of images. Based on Fast.ai MOOC Lesson 7 and https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement.

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
backbone	Optional string. Backbone CNN model to be used for creating the base of the `SuperResolution`, which is resnet34 by default. Compatible backbones: ‘SR3’, ‘SR3_UViT’, ‘resnet18’, ‘resnet34’, ‘resnet50’, ‘resnet101’, ‘resnet152’.
pretrained_path	Optional string. Path where pre-trained model is saved.

In addition to explicitly named parameters, the SuperResolution model with ‘SR3’ backbone supports the optional key word arguments:

kwargs

Parameter	Description
inner_channel	Optional int. Channel dimension. Default: 64.
norm_groups	Optional int. Group normalization. Default: 32
channel_mults	Optional list. Depth or channel multipliers. Default: [1, 2, 4, 4, 8, 8]
attn_res	Optional int. Number of attention in residual blocks. Default: 16
res_blocks	Optional int. Number of resnet block. Default: 3
dropout	Optional float. Dropout. Default: 0
schedule	Optional string. Type of noise schedule. Available types are “linear”, ‘warmup10’, ‘warmup50’, ‘const’, ‘jsd’, ‘cosine’. Default: ‘linear’
n_timestep	Optional int. Number of time-steps. Default: 1000
linear_start	Optional float. Schedule start. Default: 1e-06
linear_end	Optional float. Schedule end. Default: 1e-02

And, with ‘SR3_UViT’ backbone supports the below optional key word arguments:

patch_size	Optional int. Patch size for generating patch embeddings. Default: 16
embed_dim	Optional int. Dimension of embeddings. Default: 768
depth	Optional int. Depth of model. Default: 17
num_heads	Optional int. Number of attention heads. Default: 12
mlp_ratio	Optional float. Ratio of MLP. Default: 4.0
qkv_bias	Optional bool. Addition of bias in QK Vector. Default: False

Returns: SuperResolution Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

compute_metrics(accuracy=True, show_progress=True, **kwargs): Computes Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) on validation set.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_emd(data, emd_path)

Creates a SuperResolution object from an Esri Model Definition (EMD) file.

Parameter	Description
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.
emd_path	Required string. Path to Esri Model Definition file.

Returns: SuperResolution Object

classmethod from_model(emd_path, data=None)

Creates a SuperResolution object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: SuperResolution Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict(img_path)

Predicts and display the image.

Parameter	Description
img_path	Required path of an image.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=None, **kwargs)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.

kwargs

sampling_type	Optional string. Type of sampling. Default: ‘ddim’. keyword arguments applicable for SR3 model type only.
n_timestep	Optional int. Number of time-steps for the sampling process. Default: 200

property supported_backbones: Supported backbones for this model.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

ImageCaptioner

class arcgis.learn.ImageCaptioner(data, backbone=None, pretrained_path=None, **kwargs)

Creates an Image Captioning model.

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
backbone	Optional function. Backbone CNN model to be used for creating the encoder of the `ImageCaptioner` , which is resnet34 by default. It supports the ResNet family of backbones.
pretrained_path	Optional string. Path where pre-trained model is saved.

kwargs

Parameter

Description

decoder_params

Optional dictionary. The keys of the dictionary are embed_size, hidden_size, attention_size, teacher_forcing, dropout and pretrained_embeddings.

Default values:

decoder_params={

‘embed_size’:100,

‘hidden_size’:100,

‘attention_size’:100,

‘teacher_forcing’:1,

‘dropout’:0.1,

‘pretrained_emb’:False

}

Parameter Explanation:

‘embed_size’: Size of embedding to be used during training.
‘hidden_size’: Size of hidden layer.
‘attention_size’: Size of intermediate attention layer.
‘teacher_forcing’: Probability of teacher forcing.
‘dropout’: Dropout probability.
‘pretrained_emb’: If true, it will use fasttext embeddings.

Returns: ImageCaptioner Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

bleu_score(**kwargs)

Computes bleu score over validation set.

kwargs

Parameter	Description
beam_width	Optional int. The size of beam to be used during beam search decoding. Default is 5.
max_len	Optional int. The maximum length of the sentence to be decoded. Default is 20.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a ImageCaptioner model from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Optional fastai Databunch. Returned data object from `prepare_data()` function or None for inferencing.

Returns: ImageCaptioner Object

load(name_or_path)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict(path, visualize=True, **kwargs)

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=4, **kwargs)

Shows the ground truth and predictions of model side by side.

kwargs

Parameter	Description
beam_width	Optional int. The size of beam to be used during beam search decoding. Default is 3.
max_len	Optional int. The maximum length of the sentence to be decoded. Default is 15.

property supported_backbones: Supported torchvision backbones for this model.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

3D Models

PointCNN

class arcgis.learn.PointCNN(data, pretrained_path=None, *args, **kwargs)

Model architecture from https://arxiv.org/abs/1801.07791. Creates a Point Cloud classification model.

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
pretrained_path	Optional String. Path where pre-trained model is saved.

kwargs

Parameter	Description
encoder_params	Optional dictionary. The keys of the dictionary are out_channels, P, K, D and m. Examples: {‘out_channels’:[16, 32, 64, 96], ‘P’:[-1, 768, 384, 128], ‘K’:[12, 16, 16, 16], ‘D’:[1, 1, 2, 2], ‘m’:8 } Length of out_channels, P, K, D should be same. The length denotes the number of layers in encoder. Parameter Explanation ‘out_channels’: Number of channels produced by each layer, ‘P’: Number of points in each layer, ‘K’: Number of K-nearest neighbor in each layer, ‘D’: Dilation in each layer, ‘m’: Multiplier which is multiplied by each element of out_channel.
dropout	Optional float. This parameter will control overfitting. The range of this parameter is [0,1).
sample_point_num	Optional integer. The number of points that the model will actually process.
focal_loss	Optional boolean. If True, it will use focal loss. Default: False

Returns: PointCNN Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

compute_precision_recall(): Computes precision, recall and f1-score on the validation sets.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, **kwargs)

Train the model for the specified number of epochs and using the specified learning rates. The precision, recall and f1 scores shown in the training table are macro averaged over all classes.

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

kwargs

Parameter	Description
iters_per_epoch	Optional integer. The number of iterations to run during the training phase.

classmethod from_model(emd_path, data=None)

Creates an PointCNN model object from a Deep Learning Package(DLPK) or Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: PointCNN Object

load(name_or_path)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict_h5(path, output_path=None, **kwargs)

This method is used for infrencing using HDF file.

Parameter	Description
path	Required string. The path to folder where the HDF files which needs to be predicted are present.
output_path	Optional string. The path to folder where to dump the resulting HDF files. Defaults to results folder in input path.

kwargs

Parameter	Description
batch_size	Optional integer. The number of blocks to process in one batch. Default is set to 1.

Returns: Path where files are dumped.

predict_las(path, output_path=None, print_metrics=False, **kwargs): Note: This method has been deprecated starting from ArcGIS API for Python version 1.9.0. Use Classify Points Using Trained Model tool available in 3D Analyst extension from ArcGIS Pro 2.8 onwards.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=2, **kwargs)

Displays the results from your model on the validation set with ground truth on the left and predictions on the right. Visualization of data, exported in a geographic coordinate system is not yet supported.

Parameter	Description
rows	Optional rows. Number of rows to show. Default value is 2 and maximum value is the batch_size passed in `prepare_data()`.

kwargs

Parameter	Description
color_mapping	Optional dictionary. Mapping from class value to RGB values. Default value example: {0:[220,220,220], 2:[255,0,0], 6:[0,255,0]}.
mask_class	Optional list of integers. Array containing class values to mask. Use this parameter to display the classes of interest. Default value is []. Example: All the classes are in [0, 1, 2] to display only class 0 set the mask class parameter to be [1, 2]. List of all classes can be accessed from data.classes attribute where data is the Databunch object returned by `prepare_data()` function.
width	Optional integer. Width of the plot. Default value is 750.
height	Optional integer. Height of the plot. Default value is 512.
max_display_point	Optional integer. Maximum number of points to display. Default is 20000. A warning will be raised if the total points to display exceeds this parameter. Setting this parameter will randomly sample the specified number of points and once set, it will be used for future uses.

unfreeze(): Not implemented for this model as none of the layers are frozen by default.

RandLANet

class arcgis.learn.RandLANet(data, pretrained_path=None, *args, **kwargs)

Model architecture from https://arxiv.org/pdf/1911.11236v3.pdf. Creates RandLANet point cloud segmentation model.

Parameter	Description
data	Required fastai Databunch. Returned data object from prepare_data function.
pretrained_path	Optional String. Path where pre-trained model is saved.

kwargs

Parameter

Description

encoder_params

Optional dictionary. The keys of the dictionary are out_channels, sub_sampling_ratio, k_n.

Examples:
{‘out_channels’:[16, 64, 128, 256], ‘sub_sampling_ratio’:[4, 4, 4, 4], ‘k_n’:16 }

Length of out_channels and sub_sampling_ratio should be same. The length denotes the number of layers in encoder.

Parameter Explanation

‘out_channels’: Number of channels produced by each layer,

‘sub_sampling_ratio’: Sampling ratio of random sampling at each layer,

‘k_n’: Number of K-nearest neighbor for a point.

focal_loss

Optional boolean. If True, it will use focal loss. Default: False

Returns: RandLANet Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

compute_precision_recall(): Computes precision, recall and f1-score on the validation sets.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, **kwargs)

Train the model for the specified number of epochs and using the specified learning rates. The precision, recall and f1 scores shown in the training table are macro averaged over all classes.

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

kwargs

Parameter	Description
iters_per_epoch	Optional integer. The number of iterations to run during the training phase.

classmethod from_model(emd_path, data=None)

Creates an RandLANet model object from a Deep Learning Package(DLPK) or Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: RandLANet Object

load(name_or_path)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict_h5(path, output_path=None, **kwargs)

This method is used for infrencing using HDF file.

Parameter	Description
path	Required string. The path to folder where the HDF files which needs to be predicted are present.
output_path	Optional string. The path to folder where to dump the resulting HDF files. Defaults to results folder in input path.

kwargs

Parameter	Description
batch_size	Optional integer. The number of blocks to process in one batch. Default is set to 1.

Returns: Path where files are dumped.

predict_las(path, output_path=None, print_metrics=False, **kwargs): Note: This method has been deprecated starting from ArcGIS API for Python version 1.9.0. Use Classify Points Using Trained Model tool available in 3D Analyst extension from ArcGIS Pro 2.8 onwards.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=2, **kwargs)

Parameter	Description
rows	Optional rows. Number of rows to show. Default value is 2 and maximum value is the batch_size passed in `prepare_data()`.

kwargs

Parameter	Description
color_mapping	Optional dictionary. Mapping from class value to RGB values. Default value example: {0:[220,220,220], 2:[255,0,0], 6:[0,255,0]}.
mask_class	Optional list of integers. Array containing class values to mask. Use this parameter to display the classes of interest. Default value is []. Example: All the classes are in [0, 1, 2] to display only class 0 set the mask class parameter to be [1, 2]. List of all classes can be accessed from data.classes attribute where data is the Databunch object returned by `prepare_data()` function.
width	Optional integer. Width of the plot. Default value is 750.
height	Optional integer. Height of the plot. Default value is 512.
max_display_point	Optional integer. Maximum number of points to display. Default is 20000. A warning will be raised if the total points to display exceeds this parameter. Setting this parameter will randomly sample the specified number of points and once set, it will be used for future uses.

unfreeze(): Not implemented for this model as none of the layers are frozen by default.

SQNSeg

class arcgis.learn.SQNSeg(data, pretrained_path=None, *args, **kwargs)

Model architecture from https://arxiv.org/pdf/2104.04891.pdf. Creates SQNSeg point cloud segmentation model.

Parameter	Description
data	Required fastai Databunch. Returned data object from prepare_data function.
pretrained_path	Optional String. Path where pre-trained model is saved.

kwargs

Parameter

Description

encoder_params

Optional dictionary. The keys of the dictionary are out_channels, sub_sampling_ratio, k_n.

Examples:
{‘out_channels’:[16, 64, 128, 256], ‘sub_sampling_ratio’:[4, 4, 4, 4], ‘k_n’:16 }

Length of out_channels and sub_sampling_ratio should be same. The length denotes the number of layers in encoder.

Parameter Explanation

‘out_channels’: Number of channels produced by each layer,

‘sub_sampling_ratio’: Sampling ratio of random sampling at each layer,

‘k_n’: Number of K-nearest neighbor for a point.

focal_loss

Optional boolean. If True, it will use focal loss. Default: False

Returns: SQNSeg Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

compute_precision_recall(): Computes precision, recall and f1-score on the validation sets.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, **kwargs)

Train the model for the specified number of epochs and using the specified learning rates. The precision, recall and f1 scores shown in the training table are macro averaged over all classes.

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

kwargs

Parameter	Description
iters_per_epoch	Optional integer. The number of iterations to run during the training phase.

classmethod from_model(emd_path, data=None)

Creates an SQNSeg model object from a Deep Learning Package(DLPK) or Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: SQNSeg Object

load(name_or_path)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict_h5(path, output_path=None, **kwargs)

This method is used for infrencing using HDF file.

Parameter	Description
path	Required string. The path to folder where the HDF files which needs to be predicted are present.
output_path	Optional string. The path to folder where to dump the resulting HDF files. Defaults to results folder in input path.

kwargs

Parameter	Description
batch_size	Optional integer. The number of blocks to process in one batch. Default is set to 1.

Returns: Path where files are dumped.

predict_las(path, output_path=None, print_metrics=False, **kwargs): Note: This method has been deprecated starting from ArcGIS API for Python version 1.9.0. Use Classify Points Using Trained Model tool available in 3D Analyst extension from ArcGIS Pro 2.8 onwards.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=2, **kwargs)

Parameter	Description
rows	Optional rows. Number of rows to show. Default value is 2 and maximum value is the batch_size passed in `prepare_data()`.

kwargs

Parameter	Description
color_mapping	Optional dictionary. Mapping from class value to RGB values. Default value example: {0:[220,220,220], 2:[255,0,0], 6:[0,255,0]}.
mask_class	Optional list of integers. Array containing class values to mask. Use this parameter to display the classes of interest. Default value is []. Example: All the classes are in [0, 1, 2] to display only class 0 set the mask class parameter to be [1, 2]. List of all classes can be accessed from data.classes attribute where data is the Databunch object returned by `prepare_data()` function.
width	Optional integer. Width of the plot. Default value is 750.
height	Optional integer. Height of the plot. Default value is 512.
max_display_point	Optional integer. Maximum number of points to display. Default is 20000. A warning will be raised if the total points to display exceeds this parameter. Setting this parameter will randomly sample the specified number of points and once set, it will be used for future uses.

unfreeze(): Not implemented for this model as none of the layers are frozen by default.

MMDetection3D

class arcgis.learn.MMDetection3D(data, model='SECOND', pretrained_path=None, **kwargs)

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
model	Required model name or path to the configuration file from `MMDetection3D` repository. The list of the supported models can be queried using `supported_models`.
pretrained_path	Optional string. Path where pre-trained model is saved.

kwargs

Parameter

Description

voxel_parms

Optional dictionary. The keys of the dictionary are voxel_size, voxel_points, and max_voxels. The default value of voxel_size,`voxel_points`, and max_voxels are automatically calculated based on the ‘block size’, ‘object size’ and ‘average no. of points per block’ of the exported data.

Example:: {‘voxel_size’: [0.05, 0.05, 0.1],

‘voxel_points’: 10,

‘max_voxels’:[20000, 40000],

}

Parameter Explanation:

‘voxel_size’: List of voxel dimensions in meter [x,y,z],
‘voxel_points’: An Int, that decides the maximum number of points per voxel,
‘max_voxels’: List of maximum number of voxels in [training, validation].

Default: None.

Returns: MMDetection3D Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

average_precision_score(detect_thresh=0.2, iou_thresh=0.1, nms_overlap=0.2, mean=False, **kwargs)

Computes average precision on the validation/train set for each class.

Parameter	Description
detect_thresh	Optional float. The probability above which a detection will be considered for computing average precision. Default: 0.3.
iou_thresh	Optional float. The intersection over union threshold with the ground truth labels, above which a predicted bounding box will be considered a true positive. Default: 0.1.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive. Default: 0.01.
mean	Optional bool. If False returns class-wise average precision otherwise returns mean average precision. Default: False.

kwargs

Parameter

Description

view_type

Optional string. Dataset type to display the results.

valid - For validation set.

train - For training set.

Default: ‘valid’.

Returns: dict if mean is False otherwise float

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a MMDetection object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: MMDetection3D Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict_h5(path, output_path=None, **kwargs)

This method is used for infrencing using HDF file.

Parameter	Description
path	Required string. The path to folder where the HDF files which needs to be predicted are present.
output_path	Optional string. The path to folder where to dump the resulting HDF files. Defaults to results folder in input path.

kwargs

Parameter	Description
batch_size	Optional integer. The number of blocks to process in one batch. Default is set to 1.
detect_thresh	Optional float. The probability above which a detection will be considered valid. Default: 0.1.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive. Default: 0.6.

Returns: Path where files are dumped.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=2, detect_thresh=0.3, nms_overlap=0.01, **kwargs)

Displays the results of the trained model on a part of validation/train set. Colors of the PointCloud are only used for better visualization, and it does not depict the actual classcode colors. Visualization of data, exported in a geographic coordinate system is not yet supported.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.
detect_thresh	Optional float. The probability above which a detection will be considered valid.
nms_overlap	Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.

kwargs

Parameter	Description
color_mapping	Optional dictionary. Mapping from object id to RGB values. Colors of the PointCloud via color_mapping are only used for better visualization, and it does not depict the actual classcode colors. Default value example: {0:[220,220,220], 2:[255,0,0], 6:[0,255,0]}.
max_display_point	Optional integer. Maximum number of points to display. Default is 20000. A warning will be raised if the total points to display exceeds this parameter. Setting this parameter will randomly sample the specified number of points and once set, it will be used for future uses.
view_type	Optional string. Dataset type to display the results. `valid` - For validation set. `train` - For training set. Default: ‘valid’.

supported_models = ['SECOND']: List of models supported by this class.

unfreeze(): Not implemented for this model as none of the layers are frozen by default.

Object Tracking Models

SiamMask

class arcgis.learn.SiamMask(data=None, **kwargs)

Creates a SiamMask object.

Parameter	Description
data	Optional fastai Databunch. Returned data object from `prepare_data()` function with dataset_type as ‘ObjectTracking’ and data format as ‘YouTube-VOS’. Default value is None.

Returns: SiamMask Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

compute_metrics(iou_thres=0.2)

Computes mean IOU and f-measure on validation set.

Parameter	Description
iou_thresh	Optional float. The intersection over union threshold with the ground truth mask, above which a predicted mask will be considered a true positive.

Returns: dict with mean IOU and F-Measure

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

freeze(): Freezes the pretrained backbone.

classmethod from_model(emd_path, data=None)

Creates a SiamMask Object tracker from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: SiamMask Object

init(frame, detections, labels=None, reset=True, **kwargs)

Initializes the position of the object in the frame/Image using detections.

Parameter	Description
frame	Required numpy array. frame is used to initialize the objects to track.
detections	Required list. A list of bounding boxes.
labels	Optional list. A list of labels corresponding to the bounding boxes.

Returns: Track list

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

remove(track_ids)

Removes the tracks from the track list using track_ids

Parameter	Description
track_ids	Required List. List of track ids to be removed from the track list.

Returns: Updated track list

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5)

Displays the results of a trained model on a part of the validation set

Parameter	Description
rows	Optional int. Number of rows to display.

property supported_backbones: Supported torchvision backbones for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

update(frame, **kwargs)

Tracks the position of the object in the frame/Image

Parameter	Description
frame	Required numpy array. frame is used to update the object track.

kwargs

Parameter	Description
detections	Optional list. A list of bounding boxes.
labels	Optional list. A list of labels.

Returns: Updated track list

DeepSort

class arcgis.learn.DeepSort(data, **kwargs)

Creates a DeepSort object.

Parameter	Description
data	Fastai Databunch. Returned data object from `prepare_data()` function with dataset_type=Imagenet. Default value is None. DeepSort only supports image size of (3, 128, 64)

Returns: DeepSort Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a DeepSort Object tracker from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: DeepSort Object

init(frame, detections=None, labels=None, scores=None, **kwargs)

Initializes the DeepSort tracker for inference.

Parameter	Description
frame	Required numpy array. Frame is used to initialize the tracker.
detections	Required list. A list of bounding boxes corresponding to the detections.
labels	Optional list. A list of labels corresponding to the detections.
scores	Optional list. A list of scores corresponding to the detections.

Returns: Track list

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

remove(track_ids)

Removes the tracks from the track list using track_ids.

Parameter	Description
track_ids	Required list. list of track ids to be removed from the track list.

Returns: Updated track list

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=5)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.

property supported_backbones: Supported torchvision backbones for this model.

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

update(frame, detections=None, labels=None, scores=None, **kwargs)

Updates the DeepSort tracker.

Parameter	Description
frame	Required numpy array. Frame is used to update the tracker.
detections	Required list. A list of bounding boxes corresponding to the detections. bounding box = [xmin, ymin, width, height]
labels	Optional list. A list of labels corresponding to the detections.
scores	Optional list. A list of scores corresponding to the detections.

Returns: Track list

ObjectTracker

class arcgis.learn.ObjectTracker(tracker, detector=None, tracker_options={'detect_fail_interval': 5, 'detect_track_failure': True, 'detection_interval': 5, 'detection_threshold': 0.3, 'enable_post_processing': True, 'knn_distance_ratio': 0.75, 'min_obj_size': 10, 'recover_conf_threshold': 0.1, 'recover_iou_threshold': 0.1, 'recover_track': True, 'search_period': 60, 'stab_period': 6, 'status_fail_threshold': 0.6, 'status_history': 60, 'template_history': 25})

Creates ObjectTracker Object.

Parameter	Description
tracker	Required. Returned tracker object from from_model API of object tracking models.
detector	Optional. Returned detector object from from_model API of object detection models.
tracker_options	Optional dictionary. A dictionary with keys as parameter names and values as parameter values. “`enable_post_processing`” - refers to the flag which enables/disables post_processing of tracks internal to ObjectTracker module. For DeepSort, it’s recommended to keep this flag as False. Default - True “`detection_interval`” - refers to the interval in frames at which the detector is invoked. It should be >= 1 “`detection_threshold`” - refers to the lower threshold for selecting the detections. “`detect_track_failure`” - refers to the flag which enables/disables the logic to detect whether the object appearance has changed detection. “`recover_track`” - refers to the flag which enables/disables track recovery post failure. “`stab_period`” - refers to the number of frames after which post processing starts. “`detect_fail_interval`” - refers to the number of frames after which to detect track failure. “`min_obj_size`” - refers to the size in pixels below which tracking is assumed to have failed. “`template_history`” - refers to the number of frames before the current frame at which template image is fetched. “`status_history`” - refers to the number of frames over which status of the track is used to detect track failure. “`status_fail_threshold`” - refers to the threshold for the ratio between number of frames for which object is searched for and the total number of frames which needs to be crossed for track failure detection. “`search_period`” - refers to the number of frames for which object is searched for before declaring object is lost. “`knn_distance_ratio`” - refers to the threshold for ratio of the distances between template descriptor and the two best matched detection descriptor, used for filtering best matches. “`recover_conf_threshold`” - refers to the minimum confidence value over which recovery logic is enabled. `recover_iou_threshold` - refers to the minimum overlap between template and detection for successful recovery.

Returns: ObjectTracker Object

init(frame, detections=None, labels=None, reset=True)

Initializes tracks based on the detections returned by detector/ manually fed to the function.

Parameter	Description
frame	Required numpy array. frame is used to initialize the objects to track.
detections	Optional list. A list of bounding box to intialize the tracks.
labels	Optional list. A list of labels corresponding to the detections.
reset	Optional flag. Indicates whether to reset the tracker and remove all existing tracks before initialization.

Returns: list of active track objects

remove(tracks_ids)

Removes the tracks corresponding to track_ids parameter.

Parameter	Description
tracks_ids	Required list. List of track ids to be removed.

update(frame)

Tracks the position of the object in the frame/Image.

Parameter	Description
frame	Required numpy array. frame is the current frame to be used to track the objects.

Returns: list of active track objects

Track

class arcgis.learn.Track(id, label, bbox, mask)

Creates a Track object, used to maintain the state of a track

Parameter	Description
id	Required int. ID for each track initialized
label	Required String. label/class name of the track
bbox	Required list. Bounding box of the track
mask	Required numpy array. Mask for the tack

Returns: Track Object

Scanned Maps

ScannedMapDigitizer

class arcgis.learn.ScannedMapDigitizer(input_folder, output_folder)

Creates the object for ScannedMapDigitizer class

Parameter	Description
input_folder	Path to the folder that contains extracted maps
output_folder	Path to the folder where intermediate results should get generated

classmethod create_mask(color_list, color_delta=60, kernel_size=None, kernel_type='rect', show_result=True)

Generates the binary masked images

Parameter	Description
color_list	A list containing different color inputs in list/tuple format [(r, g, b)]. For eg: [[110,10,200], [210,108,11]].
color_delta	A value which defines the range around the threshold value for a specific color used for creating the mask images. Default value is 60.
kernel_size	A list of 2 integers corresponding to size of the morphological filter operations closing and opening respectively.
kernel_type	A string value defining the type/shape of the kernel. kernel type can be “rect”, “elliptical” or “cross”. Default value is “rect”.
show_result	A boolean value. Set to “True” to visualize results and set to “False” otherwise.

classmethod create_template_image(color, color_delta=10, kernel_size=2, show_result=True)

This method generates templates and color masks from scanned maps which are used in the subsequent step of template matching.

Parameter	Description
color	A list containing r, g, b value representing land color. The color parameter is required for extracting the land region and generating the binary mask.
color_delta	A value which defines the range around the threshold value for a specific color used for creating the mask images. Default value is 60.
kernel_size	An integer corresponding to size of kernel used for dilation(morphological operation).
show_result	A Boolean value. Set to “True” to visualize results and set to “False” otherwise.

classmethod digitize_image(show_result=True)

This method is the final step in the pipeline that maps the species regions on the search image using the computed transformations. Also, it generates the shapefiles for the species region that can be visualized using ArcGIS Pro and further edited.

Parameter	Description
show_result	A Boolean value. Set to “True” to visualize results and set to “False” otherwise.

classmethod georeference_image(padding_param, show_result=True)

This method estimates the control point pairs by traversing the contours of template image and finding the corresponding matches on the search region ROI image

Parameter	Description
padding_param	A tuple that contains x-padding and y-padding at 0th and 1st index respectively.
show_result	A Boolean value. Set to “True” to visualize results and set to “False” otherwise.

classmethod get_search_region_extent(): Getter function for search region extent

classmethod match_template_multiscale(min_scale, max_scale, num_scales, show_result=True)

This method finds the location of the best match of a smaller image (template) in a larger image(search image) assuming it exists in the larger image.

Parameter	Description
min_scale	An integer representing the minimum scale at which template matching is performed.
max_scale	An integer representing maximum scale at which template matching is performed.
num_scales	An integer representing the number of scales at which template matching is performed.
show_result	A Boolean value. Set to “True” to visualize results and set to “False” otherwise.

classmethod prepare_search_region(search_image, color, extent, image_height, image_width, show_result=True)

This method prepares the search region in which the prepared templates are to be searched.

Parameter	Description
search_image	Path to the bigger image/shapefile.
color	A list containing r, g, b value representing water color. For Eg: [173, 217, 219].
extent	Extent defines the extreme longitude/latitude of the search region.
image_height	Height of the search region.
image_width	Width of the search region.
show_result	A boolean value. Set to “True” to visualize results and set to “False” otherwise.

classmethod set_search_region_extent(extent)

Creates the object for ScannedMapDigitizer class

Parameter	Description
extent	Extent defines the extreme longitude/latitude of the search region.

Feature, Tabular and Timeseries models

FullyConnectedNetwork

class arcgis.learn.FullyConnectedNetwork(data, layers=None, emb_szs=None, **kwargs)

Creates a FullyConnectedNetwork Object. Based on the Fast.ai’s Tabular Learner

Parameter	Description
data	Required TabularDataObject. Returned data object from `prepare_tabulardata` function.
layers	Optional list, specifying the number of nodes in each layer. Default: [500, 100] is used. 2 layers each with nodes 500 and 100 respectively.
emb_szs	Optional dict, variable name with embedding size for categorical variables. If not specified, then calculated using fastai.

Returns: FullyConnectedNetwork Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

property feature_importances_

Returns: the global feature importance summary plot from SHAP.Feature is temporarily disabled.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a FullyConnectedNetwork Object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_tabulardata` function or None for inferencing.

Returns: FullyConnectedNetwork Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

predict(input_features=None, explanatory_rasters=None, datefield=None, distance_features=None, output_layer_name='Prediction Layer', gis=None, prediction_type='features', output_raster_path=None, match_field_names=None, explain=False, explain_index=None)

Predict on data from feature layer, dataframe and or raster data.

Parameter	Description
input_features	Optional `FeatureLayer` or spatially enabled dataframe. Required if prediction_type=’features’. Contains features with location and some or all fields required to infer the dependent variable value.
explanatory_rasters	Optional list of Raster Objects. If prediction_type=’raster’, must contain all rasters required to make predictions.
datefield	Optional string. Field name from feature layer that contains the date, time for the input features. Same as `prepare_tabulardata()` .
distance_features	Optional List of `FeatureLayer` objects. These layers are used for calculation of field “NEAR_DIST_1”, “NEAR_DIST_2” etc in the output dataframe. These fields contain the nearest feature distance from the input_features. Same as `prepare_tabulardata()` .
output_layer_name	Optional string. Used for publishing the output layer.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
prediction_type	Optional String. Set ‘features’ or ‘dataframe’ to make output feature layer predictions. With this feature_layer argument is required. Set ‘raster’, to make prediction raster. With this rasters must be specified.
output_raster_path	Optional path. Required when prediction_type=’raster’, saves the output raster to this path.
match_field_names	Optional dictionary. Specify mapping of field names from prediction set to training set. For example: { “Field_Name_1”: “Field_1”, “Field_Name_2”: “Field_2” }
explain	Optional Bool. Setting this parameter to true generates prediction explaination plot. Plot is generated using model interpretability library called SHAP. (https://github.com/slundberg/shap). Feature is temporarily disabled.
explain_index	Optional Int. The index of the dataframe passed to the predict function for which model interpretability is desired. If the parameter is not passed and if the explain parameter is set to true, the SHAP plot will be generated for a random index of the dataframe.

Returns: Feature Layer if prediction_type=’features’, dataframe for prediction_type=’dataframe’ else creates an output raster.

save(name_or_path, framework='PyTorch', publish=False, gis=None, save_optimizer=False, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Folder path to save the model.
framework	Optional string. Defines the framework of the model. (Only supported by `SingleShotDetector`, currently.) If framework used is `TF-ONNX`, `batch_size` can be passed as an optional keyword argument. Framework choice: ‘PyTorch’ and ‘TF-ONNX’
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

score()

Returns: R2 score for regression model and Accuracy for classification model.

show_results(rows=5)

Prints the rows of the dataframe with target and prediction columns.

Parameter	Description
rows	Optional Integer. Number of rows to print.

Returns: dataframe

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

MLModel

class arcgis.learn.MLModel(data, model_type, fairness_args=None, **kwargs)

Creates a machine learning model based on its implementation from scikit-learn, xgboost, lightgbm, catboost. For supervised learning: Refer scikit-learn, xgboost, lightgbm , catboost .

For unsupervised learning: 1. Clustering Models 2. Gaussian Mixture Models 3. Novelty and outlier detection Refer https://scikit-learn.org/stable/unsupervised_learning.html

Returns: MLModel Object

decision_function()

Returns: output from scikit-learn’s model.decision_function()

fairness_score(sensitive_feature, fairness_metrics=None, visualize=False)

Shows sample fairness score and plots for the model.

Returns: dataframe

property feature_importances_

Returns: the global feature importance summary plot from SHAP. Most of the sklearn models are supported by this method.

fit()

classmethod from_model(emd_path, data=None)

Creates a MLModel Object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Esri Model Definition file.
data	Required TabularDataObject or None. Returned data object from `prepare_tabulardata` function or None for inferencing.

Returns: MLModel Object

initialize_fair_model(fairness_args)

kneighbors(X=None, n_neighbors=None, return_distance=True)

Returns: output from scikit-learn’s model.kneighbors()

load(name_or_path)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Esri Model Definition(EMD) file.

mahalanobis()

Returns: output from scikit-learn’s model.mahalanobis()

predict(input_features=None, explanatory_rasters=None, datefield=None, distance_features=None, output_layer_name=None, gis=None, prediction_type='features', output_raster_path=None, match_field_names=None, explain=False, explain_index=None)

Predict on data from feature layer, dataframe and or raster data.

Parameter	Description
input_features	Optional `FeatureLayer` or spatial dataframe. Required if prediction_type=’features’. Contains features with location and some or all fields required to infer the dependent variable value.
explanatory_rasters	Optional list. Required if prediction_type=’raster’. Contains a list of raster objects containing some or all fields required to infer the dependent variable value.
datefield	Optional string. Field name from feature layer that contains the date, time for the input features. Same as `prepare_tabulardata()` .
distance_features	Optional List of `FeatureLayer` objects. These layers are used for calculation of field “NEAR_DIST_1”, “NEAR_DIST_2” etc in the output dataframe. These fields contain the nearest feature distance from the input_features. Same as `prepare_tabulardata()` .
output_layer_name	Optional string. Used for publishing the output layer.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
prediction_type	Optional String. Set ‘features’ or ‘dataframe’ to make output feature layer predictions. With this feature_layer argument is required. Set ‘raster’, to make prediction raster. With this rasters must be specified.
output_raster_path	Optional path. Required when prediction_type=’raster’, saves the output raster to this path.
match_field_names	Optional dictionary. Specify mapping of field names from prediction set to training set. For example: { “Field_Name_1”: “Field_1”, “Field_Name_2”: “Field_2” }
explain	Optional Bool. Setting this parameter to true generates prediction explanation plot. Plot is generated using model interpretability library called SHAP. (https://github.com/slundberg/shap)
explain_index	Optional Int. The index of the dataframe passed to the predict function for which model interpretability is desired. If the parameter is not passed and if the explain parameter is set to true, the SHAP plot will be generated for a random index of the dataframe.

Returns: FeatureLayer if prediction_type=’features’, dataframe for prediction_type=’dataframe’ else creates an output raster.

predict_proba()

Returns: output from scikit-learn’s model.predict_proba()

save(name_or_path, publish=False, gis=None, **kwargs)

Saves the model, creates an Esri Model Definition. Uses pickle to save the model. Using protocol level 2. Protocol level is backward compatible.

Returns: dataframe

score()

Returns: output from scikit-learn’s model.score(), R2 score in case of regression and Accuracy in case of classification.

For KMeans returns Opposite of the value of X on the K-means objective.

show_results(rows=5)

Shows sample results for the model.

Returns: dataframe

TimeSeriesModel

class arcgis.learn.TimeSeriesModel(data, seq_len, model_arch='InceptionTime', location_var=None, multistep=False, **kwargs)

Creates a TimeSeriesModel Object. Based on the Fast.ai’s https://github.com/timeseriesAI/timeseriesAI

Parameter	Description
data	Required TabularDataObject. Returned data object from `prepare_tabulardata` function.
seq_len	Required Integer. Sequence Length for the series. In case of raster only, seq_len = number of rasters, any other passed value will be ignored.
model_arch	Optional string. Model Architecture. Allowed “InceptionTime”, “ResCNN”, “Resnet”, “FCN”, “TimeSeriesTransformer”, “LSTM”. “LSTM” supports both “LSTM” and “Bi-LSTM”. “Bi-LSTM” is enabled by passing bidirectional=True in kwargs.
location_var	Optional string. Location variable in case of NetCDF dataset.
multistep	Optional string. It will set the model to generate more than one time-step as output in multivariate scenario. Compared to current auto-regressive fashion, it will generate multi-step output in single pass. This option is only applicable in multivariate scenario. Univariate implementation will ignore this flag. Default value is False
`**kwargs`	Optional kwargs.

Returns: TimeSeriesModel Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a TimeSeriesModel Object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_tabulardata` function or None for inferencing.

Returns: TimeSeriesModel Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

Predict on data from feature layer and or raster data.

Parameter	Description
input_features	Optional `FeatureLayer` or spatially enabled dataframe. Contains features with location of the input data. Required if prediction_type is ‘features’ or ‘dataframe’
explanatory_rasters	Optional list of Raster Objects. Required if prediction_type is ‘rasters’
datefield	Optional field_name. This field contains the date in the input_features. The field type can be a string or date time field. If specified, the field will be split into Year, month, week, day, dayofweek, dayofyear, is_month_end, is_month_start, is_quarter_end, is_quarter_start, is_year_end, is_year_start, hour, minute, second, elapsed and these will be added to the prepared data as columns. All fields other than elapsed and dayofyear are treated as categorical.
distance_features	Optional List of `FeatureLayer` objects. These layers are used for calculation of field “NEAR_DIST_1”, “NEAR_DIST_2” etc in the output dataframe. These fields contain the nearest feature distance from the input_features. Same as `prepare_tabulardata()`.
output_layer_name	Optional string. Used for publishing the output layer.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
prediction_type	Optional String. Set ‘features’ or ‘dataframe’ to make output predictions.
output_raster_path	Optional path. Required when prediction_type=’raster’, saves the output raster to this path.
match_field_names	Optional string. Specify mapping of the original training set with prediction set.
number_of_predictions	Optional int for univariate time series. Specify the number of predictions to make, adds new rows to the dataframe. For multivariate or if None, it expects the dataframe to have empty rows. if multi-step is set to True during training then it does not need empty rows. If multi-step is set to False then dataframe needs to have rows with NA values in variable predict and non-NA values in explnatory_varibles For prediction_type=’raster’, a new raster is created.

Returns: FeatureLayer/dataframe if prediction_type=’features’/’dataframe’, else returns True and saves output

raster at the specified path.

save(name_or_path, framework='PyTorch', publish=False, gis=None, save_optimizer=False, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Folder path to save the model.
framework	Optional string. Defines the framework of the model. (Only supported by `SingleShotDetector`, currently.) If framework used is `TF-ONNX`, `batch_size` can be passed as an optional keyword argument. Framework choice: ‘PyTorch’ and ‘TF-ONNX’
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

score()

Returns: R2 score for regression model and Accuracy for classification model.

show_results(rows=5)

Prints the graph with predictions.

Experimental support for multivariate timeseries.

Parameter	Description
rows	Optional Integer. Number of rows to print.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

PSETAE

class arcgis.learn.PSETAE(data, pretrained_path=None, *args, **kwargs)

Creates a Pixel-Set encoder + Temporal Attention Encoder sequence classifier.

Parameter	Description
data	Required fastai Databunch. Returned data object from prepare_data function.
pretrained_path	Optional string. Path where pre-trained model is saved.

Keyword Arguments

Parameter	Description
mlp1	Optional list. Dimensions of the successive feature spaces of MLP1. default set to [32, 64]
pooling	Optional string. Pixel-embedding pooling strategy, can be chosen in (‘mean’,’std’,’max’,’min’). default set to ‘mean’
mlp2	Optional list. Dimensions of the successive feature spaces of MLP2. default set to [128, 128]
n_head	Optional integer. Number of attention heads. default set to 4
d_k	Optional integer. Dimension of the key and query vectors. default set to 32
dropout	Optional float. dropout. default set to 0.2
T	Optional integer. Period to use for the positional encoding. default set to 1000
mlp4	Optional list. dimensions of decoder mlp .default set to [64, 32]

Returns: PSETAE Object

accuracy(): Computes overall accuracy (OA) on validation set.

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

compute_metrics(): Computes mean intersection over union (mIOU) and overall accuracy (OA) on validation set.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a PSETAE object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from prepare_data function or None for inferencing.

Returns: PSETAE Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

mIOU(): Computes mean intersection over union (mIOU) on validation set.

per_class_metrics(): Computes IoU, Precision, Recall, F1-score for all classes.

plot_losses(): Plot validation and training losses after fitting the model.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

show_results(rows=20, **kwargs)

Displays the results of a trained model on a part of the validation set.

Parameter	Description
rows	Optional int. Number of rows of results to be displayed.
total_sample_size	Optional int. Number of rows of results to be displayed.

kwargs

property supported_datasets: Supported dataset types for this model.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

Unstructured Text Models

arcgis.learn.text module

Inferencing Methods

detect_objects

arcgis.learn.detect_objects(input_raster, model, model_arguments=None, output_name=None, run_nms=False, confidence_score_field=None, class_value_field=None, max_overlap_ratio=0, context=None, process_all_raster_items=False, *, gis=None, future=False, estimate=False, **kwargs)

Function can be used to generate feature service that contains polygons on detected objects found in the imagery data using the designated deep learning model. Note that the deep learning library needs to be installed separately, in addition to the server’s built in Python 3.x library.

Note

This function is supported with ArcGIS Enterprise (Image Server) and ArcGIS Image for ArcGIS Online.

Parameter	Description
input_raster	Required. raster layer that contains objects that needs to be detected.
model	Required `Model` object.
model_arguments	Optional dictionary. Name-value pairs of arguments and their values that can be customized by the clients. eg: {“name1”:”value1”, “name2”: “value2”}
output_name	Optional. If not provided, a `FeatureLayer` is created by the method and used as the output . You can pass in an existing Feature Service Item from your GIS to use that instead. Alternatively, you can pass in the name of the output Feature Service that should be created by this method to be used as the output for the tool. A RuntimeError is raised if a service by that name already exists
run_nms	Optional bool. Default value is False. If set to True, runs the Non Maximum Suppression tool.
confidence_score_field	Optional string. The field in the feature class that contains the confidence scores as output by the object detection method. This parameter is required when you set the run_nms to True
class_value_field	Optional string. The class value field in the input feature class. If not specified, the function will use the standard class value fields Classvalue and Value. If these fields do not exist, all features will be treated as the same object class. Set only if run_nms is set to True
max_overlap_ratio	Optional integer. The maximum overlap ratio for two overlapping features. Defined as the ratio of intersection area over union area. Set only if run_nms is set to True
context	Optional dictionary. Context contains additional settings that affect task execution. Dictionary can contain value for following keys: cellSize - Set the output raster cell size, or resolution extent - Sets the processing extent used by the function parallelProcessingFactor - Sets the parallel processing factor. Default is “80%” mask: Only cells that fall within the analysis mask will be considered in the operation. Eg: {“mask”: {“url”: “<feature_service_url>”}} processorType - Sets the processor type. “CPU” or “GPU” Eg: {“processorType” : “CPU”} Setting context parameter will override the values set using arcgis.env variable for this particular function.
process_all_raster_items	Optional bool. Specifies how all raster items in an image service will be processed. False : all raster items in the image service will be mosaicked together and processed. This is the default. True : all raster items in the image service will be processed as separate images.
gis	Optional `GIS` . The GIS on which this tool runs. If not specified, the active GIS is used.
future	Keyword only parameter. Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously.
estimate	Keyword only parameter. Optional Boolean. If True, the number of credits needed to run the operation will be returned as a float. Available only on ArcGIS Online.

Returns: The output feature layer item containing the detected objects

classify_objects

arcgis.learn.classify_objects(input_raster, model, model_arguments=None, input_features=None, class_label_field=None, process_all_raster_items=False, output_name=None, context=None, *, gis=None, future=False, estimate=False, **kwargs)

Function can be used to output feature service with assigned class label for each feature based on information from overlapped imagery data using the designated deep learning model.

Note

This function is supported with ArcGIS Enterprise (Image Server) and ArcGIS Image for ArcGIS Online.

Parameter	Description
input_raster	Required. raster layer that contains objects that needs to be classified.
model	Required `Model` object.
model_arguments	Optional dictionary. Name-value pairs of arguments and their values that can be customized by the clients. eg: {“name1”:”value1”, “name2”: “value2”}
input_features	Optional `FeatureLayer`. The point, line, or polygon input feature layer that identifies the location of each object to be classified and labelled. Each row in the input feature layer represents a single object. If no input feature layer is specified, the function assumes that each input image contains a single object to be classified. If the input image or images use a spatial reference, the output from the function is a feature layer, where the extent of each image is used as the bounding geometry for each labelled feature layer. If the input image or images are not spatially referenced, the output from the function is a table containing the image ID values and the class labels for each image.
class_label_field	Optional str. The name of the field that will contain the classification label in the output feature layer. If no field name is specified, a new field called ClassLabel will be generated in the output feature layer. Example: “ClassLabel”
process_all_raster_items	Optional bool. If set to False, all raster items in the image service will be mosaicked together and processed. This is the default. If set to True, all raster items in the image service will be processed as separate images.
output_name	Optional. If not provided, a `FeatureLayer` is created by the method and used as the output . You can pass in an existing Feature Service Item from your GIS to use that instead. Alternatively, you can pass in the name of the output Feature Service that should be created by this method to be used as the output for the tool. A RuntimeError is raised if a service by that name already exists
context	Optional dictionary. Context contains additional settings that affect task execution. Dictionary can contain value for following keys: cellSize - Set the output raster cell size, or resolution extent - Sets the processing extent used by the function parallelProcessingFactor - Sets the parallel processing factor. Default is “80%” processorType - Sets the processor type. “CPU” or “GPU” Eg: {“processorType” : “CPU”} Setting context parameter will override the values set using arcgis.env variable for this particular function.
gis	Optional `GIS` . The GIS on which this tool runs. If not specified, the active GIS is used.
estimate	Keyword only parameter. Optional Boolean. If True, the number of credits needed to run the operation will be returned as a float. Available only on ArcGIS Online

Returns: The output feature layer item containing the classified objects

classify_pixels

arcgis.learn.classify_pixels(input_raster, model, model_arguments=None, output_name=None, context=None, process_all_raster_items=False, *, gis=None, future=False, estimate=False, **kwargs)

Function to classify input imagery data using a deep learning model. Note that the deep learning library needs to be installed separately, in addition to the server’s built in Python 3.x library.

Note

This function is supported with ArcGIS Enterprise (Image Server) and ArcGIS Image for ArcGIS Online.

Parameter	Description
input_raster	Required. raster layer that needs to be classified.
model	Required `Model` object.
model_arguments	Optional dictionary. Name-value pairs of arguments and their values that can be customized by the clients. eg: {“name1”:”value1”, “name2”: “value2”}
output_name	Optional. If not provided, an imagery layer is created by the method and used as the output . You can pass in an existing Image Service Item from your GIS to use that instead. Alternatively, you can pass in the name of the output Image Service that should be created by this method to be used as the output for the tool. A RuntimeError is raised if a service by that name already exists
context	Optional dictionary. Context contains additional settings that affect task execution. Dictionary can contain value for following keys: outSR - (Output Spatial Reference) Saves the result in the specified spatial reference snapRaster - Function will adjust the extent of output rasters so that they match the cell alignment of the specified snap raster. cellSize - Set the output raster cell size, or resolution extent - Sets the processing extent used by the function parallelProcessingFactor - Sets the parallel processing factor. Default is “80%” processorType - Sets the processor type. “CPU” or “GPU” Example: {“outSR” : {spatial reference}} Setting context parameter will override the values set using arcgis.env variable for this particular function.
process_all_raster_items	Optional bool. Specifies how all raster items in an image service will be processed. False : all raster items in the image service will be mosaicked together and processed. This is the default. True : all raster items in the image service will be processed as separate images.
gis	Optional `GIS` . The GIS on which this tool runs. If not specified, the active GIS is used.
future	Keyword only parameter. Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously.
estimate	Keyword only parameter. Optional Boolean. If True, the number of credits needed to run the operation will be returned as a float. Available only on ArcGIS Online.
tiles_only	Keyword only parameter. Optional boolean. In ArcGIS Online, the default output image service for this function would be a Tiled Imagery Layer. To create Dynamic Imagery Layer as output in ArcGIS Online, set tiles_only parameter to False. Function will not honor tiles_only parameter in ArcGIS Enterprise and will generate Dynamic Imagery Layer by default.

Returns: The classified imagery layer item

compute_accuracy_for_object_detection

arcgis.learn.compute_accuracy_for_object_detection(detected_features, ground_truth_features, detected_class_value_field=None, ground_truth_class_value_field=None, min_iou=None, mask_features=None, out_accuracy_table_name=None, out_accuracy_report_name=None, context=None, *, gis=None, future=False, estimate=False, **kwargs)

Function can be used to calculate the accuracy of a deep learning model by comparing the detected objects from the detect_objects function to ground truth data. Function available in ArcGIS Image Server 10.9 and higher (not available in ArcGIS Online).

Parameter	Description
detected_features	Required. The input polygon feature layer containing the objects detected from the detect_objects function.
ground_truth_features	Required. The polygon feature layer containing ground truth data.
detected_class_value_field	Optional dictionary. The field in the detected objects feature class that contains the class names or class values. If a field name is not specified, a Classvalue or Value field will be used. If these fields do not exist, all records will be identified as belonging to one class. The class values or class names must match those in the ground truth feature class exactly. Syntax: A string describing the detected class value field. Example: “class”
ground_truth_class_value_field	The field in the ground truth feature class that contains the class names or class values. If a field name is not specified, a Classvalue or Value field will be used. If these fields do not exist, all records will be identified as belonging to one class. The class values or class names must match those in the detected objects feature class exactly. Example: “class”
min_iou	The Intersection over Union (IoU) ratio to use as a threshold to evaluate the accuracy of the object-detection model. The numerator is the area of overlap between the predicted bounding box and the ground truth bounding box. The denominator is the area of union or the area encompassed by both bounding boxes. min_IoU value should be in the range 0 to 1. [0,1] Example: 0.5
mask_features	Optional `FeatureLayer`. A polygon feature service layer that delineates the area where accuracy will be computed. Only the image area that falls completely within the polygons will be assessed for accuracy.
out_accuracy_table_name	Optional. Name of the output accuracy table item to be created. If not provided, a random name is generated by the method and used as the output name.
out_accuracy_report_name	Optional. Accuracy report can either be added as an item to the portal. or can be written to a datastore. To add as an item, specify the name of the output report item (pdf item) to be created. Example: “accuracyReport” In order to write accuracy report to datastore, specify the datastore path as value to uri key. Example - “/fileShares/yourFileShareFolderName/accuracyReport”
context	Optional dictionary. Context contains additional settings that affect task execution. Dictionary can contain value for following keys: cellSize - Set the output raster cell size, or resolution extent - Sets the processing extent used by the function parallelProcessingFactor - Sets the parallel processing factor. Default is “80%” processorType - Sets the processor type. “CPU” or “GPU” Eg: {“processorType” : “CPU”} Setting context parameter will override the values set using arcgis.env variable for this particular function.
gis	Optional `GIS` . The GIS on which this tool runs. If not specified, the active GIS is used.
estimate	Keyword only parameter. Optional Boolean. If True, the number of credits needed to run the operation will be returned as a float. Available only on ArcGIS Online

Returns: The output accuracy table item or/and accuracy report item (or datastore path to accuracy report)

# Usage Example: This example generates an accuracy table for a specified minimum IoU value.

compute_accuracy_op = compute_accuracy_for_object_detection(detected_features=detected_features,
                                                            ground_truth_features=ground_truth_features,
                                                            detected_class_value_field="ClassValue",
                                                            ground_truth_class_value_field="Class",
                                                            min_iou=0.5,
                                                            mask_features=None,
                                                            out_accuracy_table_name="accuracy_table",
                                                            out_accuracy_report_name="accuracy_report",
                                                            gis=gis)

detect_change_using_deep_learning

arcgis.learn.detect_change_using_deep_learning(from_raster, to_raster, model, output_classified_raster=None, model_arguments=None, context=None, *, gis=None, future=False, estimate=False, **kwargs)

Runs a trained deep learning model to detect change between two rasters. Function available in ArcGIS Image Server 11.1 and higher.

Argument	Description
from_raster	Required ImageryLayer object. The previous raster to use for change detection.
to_raster	Required ImageryLayer object. The recent raster to use for change detection.
model	Required. The deep learning model to be used for the change detection. It can be passed as a dlpk portal item, datastore path to the Esri Model Definition (EMD) file or the EMD JSON string.
output_classified_raster	Optional String. If not provided, an Image Service is created by the method and used as the output raster. You can pass in an existing Image Service Item from your GIS to use that instead. Alternatively, you can pass in the name of the output Image Service that should be created by this method to be used as the output for the tool. A RuntimeError is raised if a service by that name already exists.
model_arguments	Optional dictionary. Name-value pairs of arguments and their values that can be customized by the clients. eg: {“name1”:”value1”, “name2”: “value2”}
context	Context contains additional settings that affect task execution. context parameter overwrites values set through arcgis.env parameter This function has the following settings: Cell size (cellSize) - Set the output raster cell size, or resolution Output Spatial Reference (outSR): The output raster will be projected into the output spatial reference. Example: {“outSR”: {spatial reference}} Extent (extent): A bounding box that defines the analysis area. Example: {“extent”: {“xmin”: -122.68, “ymin”: 45.53, “xmax”: -122.45, “ymax”: 45.6, “spatialReference”: {“wkid”: 4326}}} Parallel Processing Factor (parallelProcessingFactor): controls Raster Processing (CPU) service instances. Example: Syntax example with a specified number of processing instances: {“parallelProcessingFactor”: “2”} Syntax example with a specified percentage of total processing instances: {“parallelProcessingFactor”: “60%”}
gis	Optional GIS. The GIS on which this tool runs. If not specified, the active GIS is used.
future	Keyword only parameter. Optional Boolean. If True, the result will be a GPJob object and results will be returned asynchronously.
estimate	Keyword only parameter. Optional Boolean. If True, the number of credits needed to run the operation will be returned as a float. Available only on ArcGIS Online
folder	Keyword only parameter. Optional str or dict. Creates a folder in the portal, if it does not exist, with the given folder name and persists the output in this folder. The dictionary returned by the gis.content.create_folder() can also be passed in as input. Example: {‘username’: ‘user1’, ‘id’: ‘6a3b77c187514ef7873ba73338cf1af8’, ‘title’: ‘trial’}

Returns: The output imagery layer item

# Usage Example 1:

from_raster = gis.content.search("from_raster", item_type="Imagery Layer")[0].layers[0]
to_raster = gis.content.search("to_raster", item_type="Imagery Layer")[0].layers[0]
change_detection_model = gis.content.search("my_detection_model")[0]

detect_change_op = detect_change_using_deep_learning(from_raster=from_raster,
                                                     to_raster=to_raster,
                                                     model=change_detection_model,
                                                     gis=gis)

Embeddings

class arcgis.learn.Embeddings(dataset_type='image', backbone=None, **kwargs)

Creates an Embeddings Object. This object is capable of giving embeddings for text as well as images. The image embeddings are currently supported for RGB images only

Parameter

Description

dataset_type

Required string. The type of data for which we would like to get the embedding vectors. Valid values are text & image. Default is set to image.

Note

The image embeddings are currently supported for RGB images only.

backbone

Optional string. Specify the backbone/model-name to be used to get the embedding vectors. Default backbone for image dataset-type is resnet34 and for text dataset-type is sentence-transformers/distilbert-base-nli-stsb-mean-tokens

To learn more about the available models for for getting text embeddings, kindly visit:- https://huggingface.co/sentence-transformers

kwargs

Parameter	Description
working_dir	Option str. Path to a directory on local filesystem. If directory is not present, it will be created. This directory is used as the location to save the model.

Returns: Embeddings Object

get(text_or_list, batch_size=32, show_progress=True, return_embeddings=False, **kwargs)

Method to get the embedding vectors for the image/text items.

Parameter	Description
text_or_list	Required string or List. String containing directory path or list of directory paths where image/text files are present for which the user wants to get the embedding vectors.
batch_size	Optional integer. The number of items to process in one batch. Default is set to 32.
show_progress	Optional boolean. If set to True, will display a progress bar depicting the items processed so far. Default is set to True.
return_embeddings	Optional boolean. If set to True, a dataframe containing the embeddings will be returned. If set to False, they will be saved in a h5 file. Default is set to False.

kwargs

Parameter	Description
normalize	Optional boolean. If set to true, will normalize the image with imagenet-stats (mean and std-deviation for each color channel in RGB image). This argument is valid only for dataset-type image. Default is set to True.
file_extensions	Optional String or List. The file extension(s) for which the user wish to get embedding vectors for. Allowed values for dataset-type image are - [‘png’, ‘jpg’, ‘jpeg’, ‘tiff’, ‘tif’, ‘bmp’] Allowed values for dataset-type text are - [‘csv’, ‘txt’, ‘json’] Note For json files, if we have nested json structures, then text will be extracted only from the 1st level.
chip_size	Optional integer. Resize the image to chip_size X chip_size pixels. This argument is valid only for dataset-type image. Default is set to 224
encoding	Optional string. The encoding to read the text/csv/ json file. Applicable only for dataset-type text. Default is UTF-8
text_column	Optional string. The column that will be used to get the text content from csv or json file types. This argument is valid only for dataset-type text. Default is set to text
remove_urls	Optional boolean. If true, remove urls from text. This argument is valid only for dataset-type text. Default value is False.
remove_html_tags	Optional boolean. If true, remove html tags from text. This argument is valid only for dataset-type text. Default value is False.
pooling_strategy	Optional string. The transformer model gives embeddings for each word/token present in the text. The type of pooling to be done on those word/token vectors in order to form the text embeddings. Allowed values are - [‘mean’, ‘max’, ‘first’] This argument is valid only for dataset-type text. Default value is mean.

Returns: The path of the H5 file where items & corresponding embeddings are saved.

load(file_path, load_to_memory=True)

Load the extracted embeddings from the H5 file

Parameter	Description
file_path	Required string. The path to the H5 file which gets auto generated after the call to the get method of the `Embeddings` class
load_to_memory	Optional Bool. whether or not to load the entire content of the H5 file to memory. Loading very large H5 files into the memory takes up lot of RAM space. Use this parameter with caution for large H5 files. Default is set to True.

Returns: When load_to_memory param is True - A 2 item tuple containing the numpy arrays of extracted embeddings and items When load_to_memory param is False - A 3 item tuple containing the H5 file handler & 2 H5 dataset object of extracted embeddings and items

classmethod supported_backbones(dataset_type='image')

Get available backbones/model-name for the given dataset-type

Parameter	Description
dataset_type	Required string. The type of data for which we would like to get the embedding vectors. Valid values are text & image. Default is set to image

Returns: a list containing the available models for the given dataset-type

visualize(file_path, visualize_with_items=True, n_clusters=5, dimensions=3)

Method to visualize the embedding vectors for the image/text items. This method uses the K-Means clustering algorithm to partition the embeddings vectors into n-clusters. This requires the loading the entire content of the H5 file to RAM. Loading very large H5 files into the memory takes up lot of RAM space. Use this method with caution for large H5 files.

Parameter	Description
file_path	Required string. The path to the H5 file which gets auto generated after the call to the get method of the `Embeddings` class.
visualize_with_items	Optional Bool. Whether or not to visualize the embeddings with items. Default is set to True.
n_clusters	Optional integer. The number of clusters to create for the embedding vectors. This value will be passed to the KMeans algorithm to generate the clusters. Default is set to 5.
dimensions	Optional integer. The number of dimensions to project the embedding vectors for visualization purpose. Allowed values are 2 & 3 Default is set to 3.

Model Management

Model

class arcgis.learn.Model(model=None)

from_json(model)

Function is used to initialize Model object from model definition JSON

# Usage example

>>> model = Model()

>>> model.from_json({"Framework" :"TensorFlow",
                     "ModelConfiguration":"DeepLab",
                     "InferenceFunction":"``[functions]System\DeepLearning\ImageClassifier.py``",
                     "ModelFile":"``\\folder_path_of_pb_file\frozen_inference_graph.pb``",
                     "ExtractBands":[0,1,2],
                     "ImageWidth":513,
                     "ImageHeight":513,
                     "Classes": [ { "Value":0, "Name":"Evergreen Forest", "Color":[0, 51, 0] },
                                  { "Value":1, "Name":"Grassland/Herbaceous", "Color":[241, 185, 137] },
                                  { "Value":2, "Name":"Bare Land", "Color":[236, 236, 0] },
                                  { "Value":3, "Name":"Open Water", "Color":[0, 0, 117] },
                                  { "Value":4, "Name":"Scrub/Shrub", "Color":[102, 102, 0] },
                                  { "Value":5, "Name":"Impervious Surface", "Color":[236, 236, 236] } ] })

from_model_path(model)

Function is used to initialize Model object from url of model package or path of model definition file

# Usage Example #1:

>>> model = Model()
>>> model.from_model_path("https://xxxportal.esri.com/sharing/rest/content/items/<itemId>")

# Usage Example #2:

>>> model = Model()
>>> model.from_model_path("\\sharedstorage\sharefolder\findtrees.emd")

install(*, gis=None, future=False, **kwargs)

Function is used to install the uploaded model package (*.dlpk). Optionally after inferencing the necessary information using the model, the model can be uninstalled by uninstall_model()

Parameter	Description
gis	Optional `GIS` . The GIS on which this tool runs. If not specified, the active GIS is used.
future	Keyword only parameter. Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously.

Returns: Path where model is installed

query_info(*, gis=None, future=False, **kwargs)

Function is used to extract the deep learning model specific settings from the model package item or model definition file.

Parameter	Description
gis	Optional `GIS` . The GIS on which this tool runs. If not specified, the active GIS is used.
future	Keyword only parameter. Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously.

Returns: The key model information in dictionary format that describes what the settings are essential for this type of deep learning model.

uninstall(*, gis=None, future=False, **kwargs)

Function is used to uninstall the uploaded model package that was installed using the install_model() This function will delete the named deep learning model from the server but not the portal item.

Parameter	Description
gis	Optional `GIS` . The GIS on which this tool runs. If not specified, the active GIS is used.
future	Keyword only parameter. Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously.

Returns: itemId of the uninstalled model package item

ModelExtension

class arcgis.learn.ModelExtension(data, model_conf, backbone=None, pretrained_path=None, **kwargs)

Creates a ModelExtension object, to train the model for object detection, semantic segmentation, and edge detection.

Parameter	Description
data	Required fastai Databunch. Returned data object from `prepare_data()` function.
model_conf	A class definition contains the following methods: `get_model(self, data, backbone=None, kwargs)`: for model definition, `on_batch_begin(self, learn, model_input_batch, model_target_batch, kwargs)`: for feeding input to the model during training, `transform_input(self, xb)`: for feeding input to the model during inferencing/validation, `transform_input_multispectral(self, xb)`: for feeding input to the model during inferencing/validation in case of multispectral data, `loss(self, model_output, *model_target)`: to return loss value of the model `post_process(self, pred, nms_overlap, thres, chip_size, device)`: to post-process the output of the object-detection model. `post_process(self, pred, thres)`: to post-process the output of the segmentation model.
backbone	Optional function. If custom model requires any backbone.
pretrained_path	Optional string. Path where pre-trained model is saved.

Returns: ModelExtension Object

property available_metrics: List of available metrics that are displayed in the training table. Set monitor value to be one of these while calling the fit method.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, monitor='valid_loss', **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

Parameter	Description
epochs	Required integer. Number of cycles of training on the data. Increase it if underfitting.
lr	Optional float or slice of floats. Learning rate to be used for training the model. If `lr=None`, an optimal learning rate is automatically deduced for training the model.
one_cycle	Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used.
early_stopping	Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if parameter monitor value stops improving for 5 epochs. A minimum difference of 0.001 is required for it to be considered an improvement.
checkpoint	Optional boolean or string. Parameter to save checkpoint during training. If set to True the best model based on monitor will be saved during training. If set to ‘all’, all checkpoints are saved. If set to False, checkpointing will be off. Setting this parameter loads the best model at the end of training.
tensorboard	Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=2.1 The default value is ‘False’. Note Not applicable for Text Models
monitor	Optional string. Parameter specifies which metric to monitor while checkpointing and early stopping. Defaults to ‘valid_loss’. Value should be one of the metric that is displayed in the training table. Use {model_name}.available_metrics to list the available metrics to set here.

classmethod from_model(emd_path, data=None)

Creates a ModelExtension object from an Esri Model Definition (EMD) file.

Parameter	Description
emd_path	Required string. Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.
data	Required fastai Databunch or None. Returned data object from `prepare_data()` function or None for inferencing.

Returns: ModelExtension Object

load(name_or_path, **kwargs)

Loads a compatible saved model for inferencing or fine tuning from the disk.

Parameter	Description
name_or_path	Required string. Name or Path to Deep Learning Package (DLPK) or Esri Model Definition(EMD) file.

Keyword Arguments

Parameter	Description
strict	Optional boolean, default True. Whether to strictly enforce the keys of file`s state dict match with the model `Module.state_dict.

lr_find(allow_plot=True)

Runs the Learning Rate Finder. Helps in choosing the optimum learning rate for training the model.

Parameter	Description
allow_plot	Optional boolean. Display the plot of losses against the learning rates and mark the optimal value of the learning rate on the plot. The default value is ‘True’.

plot_losses(): Plot validation and training losses after fitting the model.

save(name_or_path, framework='PyTorch', publish=False, gis=None, compute_metrics=True, save_optimizer=False, save_inference_file=True, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

Parameter	Description
name_or_path	Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories.
framework	Optional string. Exports the model in the specified framework format (‘PyTorch’, ‘tflite’ ‘torchscript’, and ‘TF-ONXX’ (deprecated)). Only models saved with the default framework (PyTorch) can be loaded using from_model. `tflite` framework (experimental support) is supported by `SingleShotDetector` - tensorflow backend only, `RetinaNet` - tensorflow backend only.``torchscript`` format is supported by `SiamMask`, `MaskRCNN`, `SingleShotDetector`, `YOLOv3` and `RetinaNet`. For usage of SiamMask model in ArcGIS Pro >= 2.8, load the `PyTorch` framework saved model and export it with `torchscript` framework using ArcGIS API for Python >= v1.8.5. For usage of SiamMask model in ArcGIS Pro 2.9, set framework to `torchscript` and use the model files additionally generated inside ‘torch_scripts’ folder. If framework is `TF-ONNX` (Only supported for `SingleShotDetector`), `batch_size` can be passed as an optional keyword argument.
publish	Optional boolean. Publishes the DLPK as an item.
gis	Optional `GIS` Object. Used for publishing the item. If not specified then active gis user is taken.
compute_metrics	Optional boolean. Used for computing model metrics.
save_optimizer	Optional boolean. Used for saving the model-optimizer state along with the model. Default is set to False
save_inference_file	Optional boolean. Used for saving the inference file along with the model. If False, the model will not work with ArcGIS Pro 2.6 or earlier. Default is set to True.
kwargs	Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.

unfreeze(): Unfreezes the earlier layers of the model for fine-tuning.

list_models

arcgis.learn.list_models(*, gis=None, future=False, **kwargs)

Function is used to list all the installed deep learning models.

Note

This function is supported with ArcGIS Enterprise (Image Server)

Parameter	Description
gis	Optional `GIS` . The GIS on which this tool runs. If not specified, the active GIS is used.
future	Keyword only parameter. Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously.

Returns: list of deep learning models installed

train_model

arcgis.learn.train_model(input_folder, model_type, model_arguments=None, batch_size=2, max_epochs=None, learning_rate=None, backbone_model=None, validation_percent=None, pretrained_model=None, stop_training=True, freeze_model=True, overwrite_model=False, output_name=None, context=None, *, gis=None, future=False, **kwargs)

Function can be used to train a deep learning model using the output from the export_training_data function. It generates the deep learning model package (*.dlpk) and adds it to your enterprise portal. train_model function performs the training using the Raster Analytics server.

Note

This function is supported with ArcGIS Enterprise (Image Server)

Parameter	Description
input_folder	Required string or list. This is the input location for the training sample data. It can be the path of output location on the file share raster data store or a shared file system path. The training sample data folder needs to be the output of export_training_data function, containing “images” and “labels” folder, as well as the JSON model definition file written out together by the function. File share raster store and datastore path examples: /rasterStores/yourRasterStoreFolderName/trainingSampleData /fileShares/yourFileShareFolderName/trainingSampleData Shared path example: serverNamedeepLearning rainingSampleData The function also support multiple input folders. In this case, specify the list of input folders list of file share raster store and datastore path examples: [“/rasterStores/yourRasterStoreFolderName/trainingSampleDataA”, “/rasterStores/yourRasterStoreFolderName/trainingSampleDataB”] [“/fileShares/yourFileShareFolderName/trainingSampleDataA”, “/fileShares/yourFileShareFolderName/trainingSampleDataB”] list of shared path example: [“serverNamedeepLearning rainingSampleDataA”, “serverNamedeepLearning rainingSampleDataB”] Multiple input folders are supported when all the following conditions are met: The metadata format must be one of the following types: Classified_Tiles, Labeled_Tiles, Multi-labeled Tiles, PASCAL_VOC_rectangles, or RCNN_Masks. All training data must have the same metadata format. All training data must have the same number of bands. All training data must have the same tile size.
model_type	Required string. The model type to use for training the deep learning model. Possible values: SSD - The Single Shot Detector (SSD) is used for object detection. UNET - U-Net is used for pixel classification. FEATURE_CLASSIFIER - The Feature Classifier is used for object classification. PSPNET - The Pyramid Scene Parsing Network (PSPNET) is used for pixel classification. RETINANET - The RetinaNet is used for object detection. MASKRCNN - The MarkRCNN is used for object detection YOLOV3 - The YOLOv3 approach will be used to train the model. YOLOv3 is used for object detection. DeepLabV3 - The DeepLabV3 approach will be used to train the model. DeepLab is used for pixel classification. FASTERRCNN - The FasterRCNN approach will be used to train the model. FasterRCNN is used for object detection. BDCN_EDGEDETECTOR - The Bi-Directional Cascade Network (BDCN) architecture will be used to train the model. The BDCN Edge Detector is used for pixel classification. This approach is useful to improve edge detection for objects at different scales. HED_EDGEDETECTOR - The Holistically-Nested Edge Detection (HED) architecture will be used to train the model. The HED Edge Detector is used for pixel classification. This approach is useful to in edge and object boundary detection. MULTITASK_ROADEXTRACTOR - The Multi Task Road Extractor architecture will be used to train the model. The Multi Task Road Extractor is used for pixel classification. This approach is useful for road network extraction from satellite imagery. CONNECTNET - The ConnectNet architecture will be used to train the model. ConnectNet is used for pixel classification. This approach is useful for road network extraction from satellite imagery. PIX2PIX - The Pix2Pix approach will be used to train the model. Pix2Pix is used for image-to-image translation. This approach creates a model object that generates images of one type to another. The input training data for this model type uses the Export Tiles metadata format. CYCLEGAN - The CycleGAN approach will be used to train the model. CycleGAN is used for image-to-image translation. This approach creates a model object that generates images of one type to another. This approach is unique in that the images to be trained do not need to overlap. The input training data for this model type uses the CycleGAN metadata format. SUPERRESOLUTION - The Super-resolution approach will be used to train the model. Super-resolution is used for image-to-image translation. This approach creates a model object that increases the resolution and improves the quality of images. The input training data for this model type uses the Export Tiles metadata format. CHANGEDETECTOR - The Change detector approach will be used to train the model. Change detector is used for pixel classification. This approach creates a model object that uses two spatial-temporal images to create a classified raster of the change. The input training data for this model type uses the Classified Tiles metadata format. IMAGECAPTIONER - The Image captioner approach will be used to train the model. Image captioner is used for image-to-text translation. This approach creates a model that generates text captions for an image. SIAMMASK - The Siam Mask approach will be used to train the model. Siam Mask is used for object detection in videos. The model is trained using frames of the video and detects the classes and bounding boxes of the objects in each frame. The input training data for this model type uses the MaskRCNN metadata format. MMDETECTION - The MMDetection approach will be used to train the model. MMDetection is used for object detection. The supported metadata formats are PASCAL Visual Object Class rectangles and KITTI rectangles. MMSEGMENTATION - The MMSegmentation approach will be used to train the model. MMDetection is used for pixel classification. The supported metadata format is Classified Tiles. DEEPSORT - The Deep Sort approach will be used to train the model. Deep Sort is used for object detection in videos. The model is trained using frames of the video and detects the classes and bounding boxes of the objects in each frame. The input training data for this model type uses the Imagenet metadata format. Where Siam Mask is useful while tracking an object, Deep Sort is useful in training a model to track multiple objects. PIX2PIXHD - The Pix2PixHD approach will be used to train the model. Pix2PixHD is used for image-to-image translation. This approach creates a model object that generates images of one type to another. The input training data for this model type uses the Export Tiles metadata format. MAXDEEPLAB - The MAXDEEPLAB approach will be used to train the model. It is used for Panoptic Segmentation.
model_arguments	Optional dictionary. Name-value pairs of arguments and their values that can be customized by the clients. Example: {“name1”:”value1”, “name2”: “value2”}
batch_size	Optional int. The number of training samples to be processed for training at one time. If the server has a powerful GPU, this number can be increased to 16, 36, 64, and so on. Example: 4
max_epochs	Optional int. The maximum number of epochs that the model should be trained. One epoch means the whole training dataset will be passed forward and backward through the deep neural network once. Example: 20
learning_rate	Optional float. The rate at which the weights are updated during the training. It is a small positive value in the range between 0.0 and 1.0. If learning rate is set to 0, it will extract the optimal learning rate from the learning curve during the training process. Example: 0.0
backbone_model	Optional string. Specifies the preconfigured neural network to be used as an architecture for training the new model. Possible values: DENSENET121 , DENSENET161 , DENSENET169 , DENSENET201 , MOBILENET_V2 , RESNET18 , RESNET34 , RESNET50 , RESNET101 , RESNET152 , VGG11 , VGG11_BN , VGG13 , VGG13_BN , VGG16 , VGG16_BN , VGG19 , VGG19_BN , DARKNET53 , REID_V1 , REID_V2 Example: RESNET34
validation_percent	Optional float. The percentage (in %) of training sample data that will be used for validating the model. Example: 10
pretrained_model	Optional dlpk portal item. The pretrained model to be used for fine tuning the new model. It is a deep learning model package (dlpk) portal item.
stop_training	Optional bool. Specifies whether early stopping will be implemented. True - The model training will stop when the model is no longer improving, regardless of the maximum epochs specified. This is the default. False - The model training will continue until the maximum epochs is reached.
freeze_model	Optional bool. Specifies whether to freeze the backbone layers in the pretrained model, so that the weights and biases in the backbone layers remain unchanged. True - The predefined weights and biases will not be altered in the backboneModel. This is the default. False - The weights and biases of the backboneModel may be altered to better fit your training samples. This may take more time to process but usually could get better results.
overwrite_model	Optional bool. Overwrites an existing deep learning model package (.dlpk) portal item with the same name. If the output_name parameter uses the file share data store path, this overwriteModel parameter is not applied. True - The portal .dlpk item will be overwritten. False - The portal .dlpk item will not be overwritten. This is the default.
output_name	Optional. trained deep learning model package can either be added as an item to the portal or can be written to a datastore. To add as an item, specify the name of the output deep learning model package (item) to be created. Example - “trainedModel” In order to write the dlpk to fileshare datastore, specify the datastore path. Example - “/fileShares/filesharename/folder”
context	Optional dictionary. Context contains additional settings that affect task execution. Dictionary can contain value for following keys: cellSize - Set the output raster cell size, or resolution extent - Sets the processing extent used by the function parallelProcessingFactor - Sets the parallel processing factor. Default is “80%” processorType - Sets the processor type. “CPU” or “GPU” Example - {“processorType” : “CPU”} Setting context parameter will override the values set using arcgis.env variable for this particular function.
gis	Optional `GIS` . The GIS on which this tool runs. If not specified, the active GIS is used.

Returns: Returns the dlpk portal item that has properties for title, type, filename, file, id and folderId.