Train Using AutoDL (GeoAI Tools)
Summary
Trains a deep learning model by building training pipelines and automating much of the training process. This includes data augmentation, model selection, hyperparameter tuning, and batch size deduction. Its outputs include performance metrics of the best model on the training data, as well as the trained deep learning model package (.dlpk file) that can be used as input for the Extract Features Using AI Models tool to predict on new imagery.
Usage
You must install the proper deep learning framework for Python in ArcGIS Pro.
If you will be training models in a disconnected environment, see Additional Installation for Disconnected Environment for more information.
The time it takes the tool to produce the trained model depends on the following:
The amount of data provided during training
The AutoDL Mode parameter value
The Total Time Limit (Hours) parameter value
By default, the timer for all modes is set at 2 hours. The Basic mode will train the selected networks on the default backbone within the given time. The Advanced mode will divide the total time by two, perform the model evaluation in the first half, and determine the top two performing models for evaluating on other backbones in the second half. If the amount of data being trained is large, all the selected models may not be evaluated within 2 hours. In such cases, the best performing model determined within 2 hours will be considered the optimum model. You can then either use this model or rerun the tool with a higher Total Time Limit (Hours) parameter value.
This tool can also be used to fine-tune an existing trained model. For example, an existing model that has been trained for cars can be fine-tuned to train a model that identifies trucks.
To run this tool, a GPU-equipped machine is required. If you have more than one GPU, use the GPU ID environment.
The input training data for this tool must include the images and labels folders that are generated from the Export Training Data For Deep Learning tool.
Potential use cases for the tool include training object detection and pixel classification models for extracting features such as building footprint, pools, solar panels, land cover classification, and so on.
For information about requirements for running this tool and issues you may encounter, see Deep Learning frequently asked questions.
Parameters
| Label | Explanation | Data type |
|---|---|---|
|
Input Training Data |
The folders containing the image chips, labels, and statistics required to train the model. This is the output from the Export Training Data For Deep Learning tool. The metadata format of the exported data must be Classified_Tiles, PASCAL_VOC_rectangles, or KITTI_rectangles. |
Folder |
|
Output Model |
The output trained model that will be saved as a deep learning package ( |
File |
|
Pretrained Model (Optional) |
A pretrained model that will be used to fine-tune the new model. The input is an Esri model definition file ( A pretrained model with similar classes can be fine-tuned to fit the new model. The pretrained model must have been trained with the same model type and backbone model that will be used to train the new model. |
File |
|
Total Time Limit (Hours) (Optional) |
The total time limit in hours it will take for AutoDL model training. The default is 2 hours. |
Double |
|
AutoDL Mode (Optional) |
Specifies the AutoDL mode that will be used and how intensive the AutoDL search will be.
|
String |
|
Neural Networks (Optional) |
Specifies the architectures that will be used to train the model. By default, all the networks will be used.
|
String |
|
Save Evaluated Models (Optional) |
Specifies whether all evaluated models will be saved.
|
Boolean |
Derived output
| Label | Explanation | Data type |
|---|---|---|
|
Output Model File |
The output model file. |
File |
Environments
Licensing information
- Basic: No
- Standard: No
- Advanced: Requires Image Analyst