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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:
```shell
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 achieve **~1%** higher performance! See [Model_Zoo](model_zoo.md) 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:
```shell
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
```shell
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
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