Fine-tune the model

You can fine-tune the Insulator Defect Detection model to suit your geographic area, imagery, or features of interest. Fine-tuning a model requires less training data, computational resources, and time compared to training a new model.

Fine-tuning the model is recommended if you do not get satisfactory results from the available ArcGIS pretrained deep learning models. This can happen when your area of interest falls outside the applicable geographies for the models, or if your imagery properties such as resolution, scale, and seasonality are different.

You can use the Export Training Data For Deep Learning tool to prepare training data. Next, you can fine-tune this model on your data using the Train Deep Learning Model tool in ArcGIS Pro. Follow the steps below to fine-tune the model.

Prepare training data

This model is trained on three-band RGB imagery and insulator defect labels. Use the Export Training Data For Deep Learning tool to prepare training data for fine-tuning the model.

1. Browse to Tools under the Analysis tab.

   

2. Click the Toolboxes tab in the Geoprocessing pane, select Image Analyst Tools and browse to the Export Training Data For Deep Learning tool in the Deep Learning toolset.

   

3. Set the variables under the Parameters tab as follows:
   1. Input Raster—Select the three-band RGB imagery.
   2. Output Folder—Any directory of your choice on your machine.
   3. Input Feature Class Or Classified Raster Or Table (optional)—Select the labeled feature class with insulator defect training labels.
   4. Class Value Field—This is the ClassValue field referencing the class value (“1”) in the above feature class.
   5. Image Format—TIFF format
   6. Tile Size X—512
   7. Tile Size Y—512
   8. Stride X—0
   9. Stride Y—0
   10. Metadata Format—PASCAL Visual Object Classes

       

4. Set the variables under the Environments tab.
   1. Processing Extent—Select Current Display Extent or any other option from the drop-down menu as needed.
   2. Cell Size—Set the value to the desired cell size.

      

5. Click Run. Once processing is complete, the exported training data is saved in the specified directory.

Fine-tune the Insulator Defect Detection model

Complete the following steps to fine-tune the model:

1. Browse to Tools under the Analysis tab.

   

2. In the Geoprocessing pane, click the Toolboxes tab and expand Image Analyst Tools. Select the Train Deep Learning Model tool under Deep Learning.

   

3. Set the variables under the Parameters tab as follows:
   1. Input Training Data—The path to your exported training data from the previous step.
   2. Output Folder—Any directory of your choice on your machine.
   3. Max Epochs—(optional)—100 (Depending on the number of iterations you want to fine-tune the model for. Epoch is the number of iterations the tool will take to go over the data.)
   4. Batch Size— 8 (Increase or decrease this number according to your GPU capacity. It is recommended that you choose a batch size that is a square number for optimal performance.)
   5. Pre-trained Model—Input the Insulator Defect Detection (.dlpk) file downloaded from ArcGIS Living Atlas of the World.
   6. Stop when model stops improving—Checked
   7. Freeze Model—Checked

      

4. Set the variables under the Environments tab.
   1. Processor Type—Select CPU or GPU as needed. If GPU is available, it is recommended that you select GPU and set GPU ID to the GPU to be used.

      

5. Click Run. You can now use this model to run inferencing against your imagery.