A set of tools for converting a darknet dataset to COCO format working with YOLOX

Related tags

Deep Learning YOLO2COCO
Overview

darknet格式数据→COCO

  • darknet训练数据目录结构（详情参见dataset/darknet）：

    darknet
├── class.names
├── gen_config.data
├── gen_train.txt
├── gen_valid.txt
└── images
    ├── train
    └── valid

  • 转换

    python darknet2coco.py --data_path dataset/darknet/gen_config.data

YOLOV5格式数据→COCO

  • YOLOV5训练格式目录结构（详情参见dataset/YOLOV5）：

    YOLOV5
├── classes.txt
├── xxxx
│   ├── images
│   └── labels
├── train.txt
└── val.txt

  • 转换

    python yolov5_2_coco.py --dir_path dataset/YOLOV5

可视化COCO格式标注格式

python coco_visual.py --vis_num 1 \
                      --json_path dataset/YOLOV5_COCO_format/annotations/instances_train2017.json \
                      --img_dir dataset/YOLOV5_COCO_format/train2017

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Comments
  • mixed license?

    mixed license?

    In the /LICENSE file, it says the license is apache but I found that GNU license exists occasionally in the code

    Could you select one of them (personally I hope it would be apache..)?

    opened by timegate 2
  • little improvement suggestion

    little improvement suggestion

    Hello. Right now yolov5_2_coco.py script demands that train.txt paths must be absolute paths to the images. That's not very common in yolo like datasets. I suggest to change line 119 to img_path = Path(self.raw_data_dir / img_path) to use relatives paths instead or even check if the path is relative or absolute to treat it properly.

    opened by KaizerJ 1
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