num_classes issue

[virgile-fsr]

rf-detr/rfdetr/main.py

Line 92 in 6ca1b58

self.reinitialize_detection_head(checkpoint_num_classes)

If user creates a new model with:
model = RFDETRBase(num_classes=123)
The code linked above will overwrite the num classes specified by the users by the one in the checkpoint (90 by default). I don't think this is expected behavior. More worrisome, the config of the object stays with num_classes=123 which will cause issues in the train function.

If you call: model.train(dataset_dir=...) with the model above, it will check the number of classes in the dataset and compare it with self.model_config.num_classes which is still 123 in our example (even though the actual model has a 90 sized output layer). So if the number of classes in the dataset is indeed 123, the check will pass even though it should not.

A different issue it that if you try a dataset with a single class, the labels from the dataloader will have size zero.

[isaacrob-roboflow]

single class is being discussed here #48, the issue is due to our working mainly with datasets exported from roboflow which have a filler 0 class causing the issue you're observing with non-roboflow format datasets.

good point re using number of classes as an excuse to reinitialize the head! do you have an idea of what a preferred interface would look like? maybe something like having num_classes default to None and if it is not None, we reinitialize the head with that number of classes?