yolox-pytorch

A pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in 2021"

1. Notes

This is a pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in 2021" [https://arxiv.org/abs/2107.08430]
The repo is still under development

2. Environment

pytorch>=1.7.0, python>=3.6, Ubuntu/Windows, see more in 'requirements.txt'

cd /path/to/your/work
git clone https://github.com/zhangming8/yolox-pytorch.git
cd yolox-pytorch
download pre-train weights in Model Zoo to /path/to/your/work/weights

3. Object Detection

Model Zoo

All weights can be downloaded
from GoogleDrive
or BaiduDrive (code:bc72)

Model test size mAPval
0.5:0.95		 mAPtest
0.5:0.95		 Params
(M)
yolox-nano 416 25.4 25.7 0.91
yolox-tiny 416 33.1 33.2 5.06
yolox-s 640 39.3 39.6 9.0
yolox-m 640 46.2 46.4 25.3
yolox-l 640 49.5 50.0 54.2
yolox-x 640 50.5 51.1 99.1
yolox-x 800 51.2 51.9 99.1

mAP was reevaluated on COCO val2017 and test2017, and some results are slightly better than the official
implement YOLOX. You can reproduce them by scripts in 'evaluate.sh'

Dataset

download COCO:
http://images.cocodataset.org/zips/train2017.zip
http://images.cocodataset.org/zips/val2017.zip
http://images.cocodataset.org/annotations/annotations_trainval2017.zip

unzip and put COCO dataset in following folders:
/path/to/dataset/annotations/instances_train2017.json
/path/to/dataset/annotations/instances_val2017.json
/path/to/dataset/images/train2017/*.jpg
/path/to/dataset/images/val2017/*.jpg

change opt.dataset_path = "/path/to/dataset" in 'config.py'

Train

See more example in 'train.sh'
a. Train from scratch:(backbone="CSPDarknet-s" means using yolox-s, and you can change it, eg: CSPDarknet-nano, tiny, s, m, l, x)
python train.py gpus='0' backbone="CSPDarknet-s" num_epochs=300 exp_id="coco_CSPDarknet-s_640x640" use_amp=True val_intervals=2 data_num_workers=6 batch_size=48

b. Finetune, download pre-trained weight on COCO and finetune on customer dataset:
python train.py gpus='0' backbone="CSPDarknet-s" num_epochs=300 exp_id="coco_CSPDarknet-s_640x640" use_amp=True val_intervals=2 data_num_workers=6 batch_size=48 load_model="../weights/yolox-s.pth"

c. Resume, you can use 'resume=True' when your training is accidentally stopped:
python train.py gpus='0' backbone="CSPDarknet-s" num_epochs=300 exp_id="coco_CSPDarknet-s_640x640" use_amp=True val_intervals=2 data_num_workers=6 batch_size=48 load_model="exp/coco_CSPDarknet-s_640x640/model_last.pth" resume=True

Some Tips:

a. You can also change params in 'train.sh'(these params will replace opt.xxx in config.py) and use 'nohup sh train.sh &' to train
b. Multi-gpu train: set opt.gpus = "3,5,6,7" in 'config.py' or set gpus="3,5,6,7" in 'train.sh'
c. If you want to close multi-size training, change opt.random_size = None in 'config.py' or set random_size=None in 'train.sh'
d. random_size = (14, 26) means: Randomly select an integer from interval (14,26) and multiply by 32 as the input size
e. Visualized log by tensorboard: 
    tensorboard --logdir exp/your_exp_id/logs_2021-08-xx-xx-xx and visit http://localhost:6006
   Your can also use the following shell scripts:
    (1) grep 'train epoch' exp/your_exp_id/logs_2021-08-xx-xx-xx/log.txt
    (2) grep 'val epoch' exp/your_exp_id/logs_2021-08-xx-xx-xx/log.txt

Evaluate

Module weights will be saved in './exp/your_exp_id/model_xx.pth'
change 'load_model'='weight/path/to/evaluate.pth' and backbone='backbone-type' in 'evaluate.sh'
sh evaluate.sh

Predict/Inference/Demo

a. Predict images, change img_dir and load_model
python predict.py gpus='0' backbone="CSPDarknet-s" vis_thresh=0.3 load_model="exp/coco_CSPDarknet-s_640x640/model_best.pth" img_dir='/path/to/dataset/images/val2017'

b. Predict video
python predict.py gpus='0' backbone="CSPDarknet-s" vis_thresh=0.3 load_model="exp/coco_CSPDarknet-s_640x640/model_best.pth" video_dir='/path/to/your/video.mp4'

You can also change params in 'predict.sh', and use 'sh predict.sh'

Train Customer Dataset(VOC format)

1. put your annotations(.xml) and images(.jpg) into:
    /path/to/voc_data/images/train2017/*.jpg  # train images
    /path/to/voc_data/images/train2017/*.xml  # train xml annotations
    /path/to/voc_data/images/val2017/*.jpg  # val images
    /path/to/voc_data/images/val2017/*.xml  # val xml annotations

2. change opt.label_name = ['your', 'dataset', 'label'] in 'config.py'
   change opt.dataset_path = '/path/to/voc_data' in 'config.py'

3. python tools/voc_to_coco.py
   Converted COCO format annotation will be saved into:
    /path/to/voc_data/annotations/instances_train2017.json
    /path/to/voc_data/annotations/instances_val2017.json

4. (Optional) you can visualize the converted annotations by:
    python tools/show_coco_anns.py
    Here is an analysis of the COCO annotation https://blog.csdn.net/u010397980/article/details/90341223?spm=1001.2014.3001.5501

5. run train.sh, evaluate.sh, predict.sh (are the same as COCO)

4. Multi/One-class Multi-object Tracking(MOT)

one-class/single-class MOT Dataset

DOING

Multi-class MOT Dataset

DOING

Train

DOING

Evaluate

DOING

Predict/Inference/Demo

DOING

5. Acknowledgement

https://github.com/Megvii-BaseDetection/YOLOX
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/open-mmlab/mmdetection
https://github.com/xingyizhou/CenterNet

GitHub

https://github.com/zhangming8/yolox-pytorch