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

Last update: Jan 3, 2023

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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

相关资料

MSCOCO数据标注详解

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Comments

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

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

Owner

RapidAI-NG

A open source organization for the development of AI applications.

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