README_EN.md

Useful tools for computer vision/deep learning.

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Usage

yolo2coco.py

Transform YOLO format dataset to COCO format.$ROOT_PATH is the root directory. Please organize your files according to the following:

└── $ROOT_PATH

  ├── classes.txt

  ├── images

  └──labels

• classes.txt , the statement of class. one class per line.

• images , the directory should contain all the images you want to train(support png and jpg)

• labels , the directory should contain all the lables(Same name as the images, txt format)

Run python yolo2coco.py --root_dir $ROOT_PATH ，and you will see a dir: annotations.

About the argument

• --root_path path of $ROOT_PATH
• --random_split whether to randomly split the datasete. If store ture, dir annotations will include train.json val.json test.json （split to 8:1:1）
• --save_path save name of output,default is train.json

coco2yolo.py

Read the label in JSON format of coco dataset and output the label for Yolo training. It should be noted that the categories ID in the official dataset of coco2017 is not continuous, which will cause problems when Yolo reads it, so it needs to be remapped. This script will map the class id from 0 to 79(If it is your own dataset, it will be remapped.)

Run: python coco2yolo.py --json_path $JSON_FILE_PATH --save_path $LABEL_SAVE_PATH

• $JSON_FILE_PATH path of json
• $JSON_FILE_PATH output directory（defult is ./labels)