Pytorch 轻松重新实现“YOLOX2021 年超越 YOLO 系列”.zip

## 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](https://drive.google.com/drive/folders/1qEMLzikH5JwRNRoHpeCa6BJBeSQ6xXCH?usp=sharing) or [BaiduDrive](https://pan.baidu.com/s/1UsbdnyVwRJhr9Vy1tmJLeQ) (code:bc72) |Model |test size |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 | Params<br>(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](https://github.com/Megvii-BaseDetection/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