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YOLO2COCO

English | 简体中文

labelImg label data → YOLOV5 format

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  • Convert the yolo data format marked by the labelImg library to YOLOV5 format data with one click

  • The labelImg label data directory structure is as follows (see dataset/labelImg_dataset for details):

      labelImg_dataset
  ├── classes.txt
  ├── images(13).jpg
  ├── images(13).txt
  ├── images(3).jpg
  ├── images(3).txt
  ├── images4.jpg
  ├── images4.txt
  ├── images5.jpg
  ├── images5.txt
  ├── images6.jpg
  ├── images7.jpg
  └── images7.txt

  • Convert

    python labelImg_2_yolov5.py --src_dir dataset/labelImg_dataset \
                            --out_dir dataset/labelImg_dataset_output \
                            --val_ratio 0.2 \
                            --have_test true \
                            --test_ratio 0.2
    • --src_dir: the directory where labelImg is stored after labeling.
    • --out_dir: the location where the data is stored after conversion.
    • --val_ratio: the ratio of the generated validation set to the whole data, default is 0.2.
    • --have_test: whether to generate the test part of the data, the default is True.
    • --test_ratio: percentage of the whole data of the test data, default is 0.2.

  • Converted directory structure (see dataset/labelImg_dataset_output for details):

    labelImg_dataset_output/
  ├── classes.txt
  ├── images
  │   ├── images(13).jpg
  │   ├── images(3).jpg
  │   ├── images4.jpg
  │   ├── images5.jpg
  │   └── images7.jpg
  ├── labels
  │   ├── images(13).txt
  │   ├── images(3).txt
  │   ├── images4.txt
  │   ├── images5.txt
  │   └── images7.txt
  ├── non_labels        # This is the catalog without the labeled images.
  │   └── images6.jpg
  ├── test.txt
  ├── train.txt
  └── val.txt

  • You can further directly convert the dataset/labelImg_dataset_output directory to COCO

    python yolov5_2_coco.py --dir_path dataset/labellImg_dataset_output

YOLOV5 format data → COCO

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  • Some background images can be added to the training by directly placing them into the backgroud_images directory.

  • The conversion program will automatically scan this directory and add it to the training set, allowing seamless integration with subsequent YOLOX training.

  • YOLOV5 training format directory structure (see dataset/YOLOV5 for details).

    YOLOV5
├── classes.txt
├── background_images  # usually images that are easily confused with the object to be detected
│   └── bg1.jpeg
├── images
│   ├── images(13).jpg
│   └── images(3).jpg
├── labels
│   ├── images(13).txt
│   └── images(3).txt
├── train.txt
└── val.txt

  • Convert

    python yolov5_2_coco.py --dir_path dataset/YOLOV5 --mode_list train,val
    • --dir_path: the directory where the collated dataset is located
    • --mode_list: specify the generated json, provided that there is a corresponding txt file, which can be specified separately. (e.g. -train,val,test)

  • The structure of the converted directory (see dataset/YOLOV5_COCO_format for details)

    YOLOV5_COCO_format
├── annotations
│   ├── instances_train2017.json
│   └── instances_val2017.json
├── train2017
│   ├── 000000000001.jpg
│   └── 000000000002.jpg  # This is the background image.
└── val2017
    └── 000000000001.jpg

YOLOV5 YAML description file → COCO

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  • The YOLOV5 yaml data file needs to contain.

    YOLOV5_yaml
├── images
│   ├── train
│   │   ├── images(13).jpg
│   │   └── images(3).jpg
│   └── val
│       ├── images(13).jpg
│       └── images(3).jpg
├── labels
│   ├── train
│   │   ├── images(13).txt
│   │   └── images(3).txt
│   └── val
│       ├── images(13).txt
│       └── images(3).txt
└── sample.yaml

  • Convert

    python yolov5_yaml_2_coco.py --yaml_path dataset/YOLOV5_yaml/sample.yaml

darknet format data → COCO

  • Darknet training data directory structure (see dataset/darknet for details).

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

  • Convert

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

Visualize images in COCO format

Click to expand 
python coco_visual.py --vis_num 1 \
                    --json_path dataset/YOLOV5_COCO_format/annotations/instances_train2017.json \
                    --img_dir dataset/YOLOV5_COCO_format/train2017
  • --vis_num: specify the index of the image to be viewed
  • --json_path: path to the json file of the image to view
  • --img_dir: view the directory where the image is located

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