Support of Incremental Object Detection

Hello
How are you?
Thanks for contributing this project.
I have a question.
Does this project support class-incremental training for saving training time without catastrophic forgetting?
Let's suppose I've trained a model on the dataset with K classes for 7 days.
If a new class is added into this dataset, should we train a model with the expanded dataset (K+1 classes) from begin?
If so, it is so expensive, especially in case of object detection in retail store.
That's because a new class of good is added frequently in retail store.
Can we train a new model in short time with the original weight on the expanded dataset?
I think that this is a very important function.
I send some recommended papers for this project.
https://arxiv.org/pdf/1708.06977.pdf
https://arxiv.org/pdf/2003.04668.pdf
https://arxiv.org/pdf/2003.06957.pdf
https://arxiv.org/pdf/2003.07304.pdf
Please let me know if you have a willing to implement this.
Thanks

The issue names no repository files, tests, or entry points. Start by reviewing the existing training workflow alongside the cited class-incremental learning papers; done would mean a defined approach for adding classes from existing weights while retaining prior-class performance without full retraining.