hyperscypion / yolox-pytorch Goto Github PK

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

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

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'

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

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

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

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'

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)

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