An example of running a training job can be found in train_segmentation.sh.
Make sure to include your w&b API key and adjust any filepaths and hyperparameters here.
Modify the docker/run.sh file to mount your data to the container.
Build the runtime container using the helper script ./docker/build.sh.
Run the container with docker/run.sh.
To add a custom dataset you need to do a few things.
First define the dataset class and add it to the build_dataset() function in datasets/segmentation.py by adding it as a return in the switch statement.
Next add the revelant information to the db_info dictionary in datasets/segmentation.py.
An example of a valid entry would be:
"cityscapes": {
"n_classes": 19,
"size": [768, 768], # [height, width]
"ignore_index": -100,
"class_labels": CITYSCAPES_SEG_CLASSES,
"normalisation": [[0.288, 0.327, 0.286], [0.190, 0.190, 0.187]],
}
You can leave the "class_labels" field for the dataset equal to None, this will log each classes IoU as a number from [0, n_classes-1].
If you want to have IoU logged with the pixel classifications natural language name you can define a dictionary in containing the details of each class.
An example of PascalVOC can be found in /datasets/constants.py.
Once the dataset if defined, added to build_dataset() and has an entry in db_info you can use it in train_segmentation.sh as the --dataset-name.
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