MegEngine implementation of YOLOX
YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. For more details, please refer to our report on Arxiv.
This repo is an implementation of MegEngine version YOLOX, there is also a PyTorch implementation.
Model | size | mAPval 0.5:0.95 |
Params (M) |
FLOPs (G) |
weights |
---|---|---|---|---|---|
YOLOX-Tiny | 416 | 32.2 | 5.06 | 6.45 | github |
Comming soon!
Step1. Install YOLOX.
git clone [email protected]:MegEngine/YOLOX.git
cd YOLOX
pip3 install -U pip && pip3 install -r requirements.txt
pip3 install -v -e . # or python3 setup.py develop
Step2. Install pycocotools.
pip3 install cython; pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
Step1. Download a pretrained model from the benchmark table.
Step2. Use either -n or -f to specify your detector's config. For example:
python tools/demo.py image -n yolox-tiny -c /path/to/your/yolox_tiny.pkl --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 416 --save_result --device [cpu/gpu]
or
python tools/demo.py image -f exps/default/yolox_tiny.py -c /path/to/your/yolox_tiny.pkl --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 416 --save_result --device [cpu/gpu]
Demo for video:
python tools/demo.py video -n yolox-s -c /path/to/your/yolox_s.pkl --path /path/to/your/video --conf 0.25 --nms 0.45 --tsize 416 --save_result --device [cpu/gpu]
Step1. Prepare COCO dataset
cd <YOLOX_HOME>
ln -s /path/to/your/COCO ./datasets/COCO
Step2. Reproduce our results on COCO by specifying -n:
python tools/train.py -n yolox-tiny -d 8 -b 128
When using -f, the above commands are equivalent to:
python tools/train.py -f exps/default/yolox-tiny.py -d 8 -b 128
We support batch testing for fast evaluation:
python tools/eval.py -n yolox-tiny -c yolox_tiny.pkl -b 64 -d 8 --conf 0.001 [--fuse]
To reproduce speed test, we use the following command:
python tools/eval.py -n yolox-tiny -c yolox_tiny.pkl -b 1 -d 1 --conf 0.001 --fuse
NOTE: result model is dumped with optimize_for_inference
and enable_fuse_conv_bias_nonlinearity
.
python3 tools/export_mge.py -n yolox-tiny -c yolox_tiny.pkl --dump_path yolox_tiny.mge
Model Info: yolox-s @ input(1,3,640,640)
Testing Devices
x86_64 -- Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
AArch64 -- Xiaomi phone mi9
CUDA -- 1080TI @ cuda-10.1-cudnn-v7.6.3-TensorRT-6.0.1.5.sh @ Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
[email protected] +fastrun +weight_preprocess (msec) | 1 thread | 2 thread | 4 thread | 8 thread |
---|---|---|---|---|
x86_64(fp32) | 516.245 | 318.29 | 253.273 | 222.534 |
x86_64(fp32+chw88) | 362.020 | NONE | NONE | NONE |
aarch64(fp32+chw44) | 555.877 | 351.371 | 242.044 | NONE |
aarch64(fp16+chw) | 439.606 | 327.356 | 255.531 | NONE |
CUDA @ CUDA (msec) | 1 batch | 2 batch | 4 batch | 8 batch | 16 batch | 32 batch | 64 batch |
---|---|---|---|---|---|---|---|
megengine(fp32+chw) | 8.137 | 13.2893 | 23.6633 | 44.470 | 86.491 | 168.95 | 334.248 |
If you use YOLOX in your research, please cite our work by using the following BibTeX entry:
@article{yolox2021,
title={YOLOX: Exceeding YOLO Series in 2021},
author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
journal={arXiv preprint arXiv:2107.08430},
year={2021}
}