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Description
Add an option to select specific dataset classes to use during training, without requiring users to create a separate filtered dataset.
For example:
model.train(
dataset_dir="dataset",
only_classes=[0, 4], # new feature
)Original classes:
0 → person
1 → car
2 → bicycle
3 → dog
4 → cat
5 → truck
only_classes = [0, 4]
After filtering/remapping:
0 → person
1 → cat
Use case
I'm working with a very large multi-class dataset and sometimes need to train a model on only a subset of the available classes. Creating a separate filtered copy of the dataset for each experiment can be time- and storage-consuming.
Being able to select the required classes directly during training would make this workflow much more convenient.
Additional
I couldn't find an existing training parameter that provides this functionality, so I believe this could be a useful addition to RF-DETR.
I'm happy to work on the implementation and tests if the maintainers think this would be a good fit.
Are you willing to submit a PR?
Search before asking
Description
Add an option to select specific dataset classes to use during training, without requiring users to create a separate filtered dataset.
For example:
Original classes:
0 → person
1 → car
2 → bicycle
3 → dog
4 → cat
5 → truck
only_classes = [0, 4]
After filtering/remapping:
0 → person
1 → cat
Use case
I'm working with a very large multi-class dataset and sometimes need to train a model on only a subset of the available classes. Creating a separate filtered copy of the dataset for each experiment can be time- and storage-consuming.
Being able to select the required classes directly during training would make this workflow much more convenient.
Additional
I couldn't find an existing training parameter that provides this functionality, so I believe this could be a useful addition to RF-DETR.
I'm happy to work on the implementation and tests if the maintainers think this would be a good fit.
Are you willing to submit a PR?