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Updates notes

【2021/08/19】

  • Support image caching for faster training, which requires large system RAM.
  • Remove the dependence of apex and support torch amp training.
  • Optimize the preprocessing for faster training
  • Replace the older distort augmentation with new HSV aug for faster training and better performance.

2X Faster training

We optimize the data preprocess and support image caching with --cache flag:

python tools/train.py -n yolox-s -d 8 -b 64 --fp16 -o [--cache]
                         yolox-m
                         yolox-l
                         yolox-x
  • -d: number of gpu devices
  • -b: total batch size, the recommended number for -b is num-gpu * 8
  • --fp16: mixed precision training
  • --cache: caching imgs into RAM to accelarate training, which need large system RAM.

Higher performance

New models achive ~1% higher performance! See Model_Zoo for more details.

Support torch amp

We now support torch.cuda.amp training and Apex is not used anymore.

Breaking changes

We remove the normalization operation like -mean/std. This will make the old weights incompatible.

If you still want to use old weights, you can add `--legacy' in demo and eval:

python tools/demo.py image -n yolox-s -c /path/to/your/yolox_s.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu] [--legacy]

and

python tools/eval.py -n  yolox-s -c yolox_s.pth -b 64 -d 8 --conf 0.001 [--fp16] [--fuse] [--legacy]
                         yolox-m
                         yolox-l
                         yolox-x

But for deployment demo, we don't support the old weights anymore. Users could checkout to YOLOX version 0.1.0 to use legacy weights for deployment