Are you sure you want to delete this access key?
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
New models achieve ~1% higher performance! See Model_Zoo for more details.
We now support torch.cuda.amp training and Apex is not used anymore.
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
Press p or to see the previous file or, n or to see the next file
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?