README.md · 申肖阳/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](https://drive.google.com/drive/folders/1qEMLzikH5JwRNRoHpeCa6BJBeSQ6xXCH?usp=sharing) or [BaiduDrive](https://pan.baidu.com/s/1UsbdnyVwRJhr9Vy1tmJLeQ)(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 |

The weights were converted from [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX). mAP was reevaluated on COCO val2017 and test2017, and some results are slightly better than the official implement. 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
    
    a. Train from scratch:(backbone="CSPDarknet-s" means using yolox-s, and you can change it to any other backbone, 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=False val_intervals=1 data_num_workers=8
    
    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=False val_intervals=1 data_num_workers=8 load_model="../weights/yolox-s.pth" resume=False
    
    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=False val_intervals=1 data_num_workers=8 load_model="exp/coco_CSPDarknet-s_640x640/model_last.pth" resume=True
    
    d. Some tips:
    Ⅰ You can also change params in 'train.sh'(these params will replace opt.xxx in config.py) and use 'sh train.sh' to train
    Ⅱ if you want to close mulit-size training, change opt.random_size = None or (20, 21) in 'config.py')
    Ⅲ mulit-gpu train: change opt.gpus = "3,5,6,7"
    
#### Evaluate

The trained 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, not ready
    1. download and unzip VisDrone dataset http://aiskyeye.com/download/multi-object-tracking_2021
    
    2. put train and val dataset into:
    /path/to/dataset/VisDrone/VisDrone2019-MOT-train  # This folder contains two subfolders, 'annotations' and 'sequences'
    /path/to/dataset/VisDrone/VisDrone2019-MOT-val  # This folder contains two subfolders, 'annotations' and 'sequences'
    
    3. change opt.dataset_path = "/path/to/dataset/VisDrone" in 'config.py'
    4. python tools/visdrone_mot_to_coco.py  # converted to COCO format
    5.(Optional) python tools/show_coco_anns.py  # visualized tracking id
    
    6. set class name and tracking id number
    change opt.label_name=['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor']
    change opt.tracking_id_nums=[1829, 853, 323, 3017, 295, 159, 215, 79, 55, 749]
    change opt.reid_dim=128

#### 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

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