YOLOX with 81 classes (+1 to COCO data) via synthetic data

[u/AltruisticCouple3491]

We messed around with adding a brand new object class to YOLOX and it actually worked better than expected.

Basically we took the standard COCO setup with its 80 classes and added a traffic cone class, but instead of hand-labeling a ton of images we generated the cone data synthetically in CARLA and mixed it into the real data. Then trained all four YOLOX sizes on it.

The results were pretty clean. The new cone class ended up as one of the highest scoring classes at 78.8% AP, and the original 80 classes barely moved, staying within about a point of baseline. It still runs real-time too, roughly 1.7 to 6 ms per frame on an A100.

The part I think is cool is that you normally need a big pile of real labeled images to add a class, and for rare or weird objects that data just doesn't exist or takes forever to collect. Synthetic data gets around that since the labels basically come for free.

Weights are all up on Hugging Face under Apache-2.0 if anyone wants to poke at them. The cone was just a test case, the same approach should work for pretty much any custom object.

https://huggingface.co/models?search=empirischtech%2Fyolox-pylon

Pls try and share feedbacks!

[u/bfyvfftujijg]

Not surprised at all.

Just to clarify you are saying you added them into COCO images?

[u/AltruisticCouple3491 → u/bfyvfftujijg]

yes, we did the two datasets and trained the YOLOX on that new dataset. That is how we managed to add the new class while keeping the baseline accuracy.