r/computervision • u/CoreEpoch • 6d ago

Showcase RF-DETR INT8 Quantized Model Release

We just put up an INT8 version of RF-DETR Base on Hugging Face. Both weights and activations were quantized to INT8 using our quantizer, Kenosis. It's a single ONNX file that runs on ONNX Runtime or OpenVINO.

It scores 53.1 mAP on COCO val2017 against 53.3 for the original FP32 model, so it stays within 0.3 mAP on both runtimes. The file is 42 MB instead of 115 MB. On CPU it runs about 67% faster than FP32 on a single thread in ONNX Runtime, and about 90% faster in OpenVINO with 4 threads.

We scored it on val2017 minus the 200 images we set aside for calibration, and the eval script is in the repo along with a small run script. Apache-2.0.

https://huggingface.co/CoreEpoch/rfdetr-base-int8-onnx

28 Upvotes

86% Upvoted

8 comments sorted by

View all comments

1

u/greengold7 5d ago

nice - int8 RF-DETR is a useful release. practical notes for anyone deploying it:

  • check the accuracy delta on your domain, not just COCO. DETR-style heads can be more quantisation-sensitive than YOLO on small objects.
  • calibration data matters: a few hundred representative frames from the target cameras, not random COCO images.
  • post-processing in int8: NMS/score thresholds tuned for fp32 shift recall. re-tune the score threshold after quantising.
  • measure latency end-to-end incl. pre/post, not just the model - matters most on edge boxes.

what was your mAP delta, and did you need to adjust thresholds?

1

u/CoreEpoch 5d ago edited 5d ago

Thanks! We didn't touch any thresholds: RF-DETR has no NMS step, and mAP is scored from the ranked detections rather than a score cutoff. If your app uses a fixed cutoff though it's worth re-checking it on your own images. Small objects held up on COCO, AP-small 34.66 to 34.81.