We make the dataset for ultralytics-version yolo (https://github.com/ultralytics/yolov3 )
And the origianl darknet-version yolo (https://github.com/AlexeyAB/darknet ),
Both support for yolov3 and yolov4.
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Part I: make an environment for the Darknet
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Part II: make_VOC_dataset
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Part III: make_COCO_dataset and test on the test2017.
- Pelease read about the ./Env_For_Darknet.md
- Make_VOC_dataset : this three python files realized the function of convert the label from the ".xml" format to the ".txt" format, so that we can train the YOLO with the .txt format label.
- If we make it out, the VOC dataset in the data folder should be the following hierarchy.
├── data
│ ├── VOC For VOC dataset folder
│ | ├── images
| | | ├── train
| | | ├── val
| | ├── labels
| | | ├── train
| | | ├── val
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3.1 make_COCO_dataset:
The first three python files realized the function of convert the label from the " .json" format to the ".txt" format, so that we can train the YOLO network with the .txt format label.
-If we make it out, the COCO dataset in the data folder should be the following hierarchy.
├── data
│ ├── COCO For COCO dataset folder
│ | ├── images
| | | ├── train
| | | ├── val
| | ├── labels
| | | ├── train
| | | ├── val
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3.2 If you want test your model without the ground truth label.
And submit the reuslts.json to the CodaLab https://competitions.codalab.org/competitions/20794 ,
Here we have a simple tutorials: ./make_COCO_dataset/readme_test-dev2017.md
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For the COCO dataset and yolovv3-v4 pretrained weights: Download the following address, download and unzip the folder to copy to the data directory can be used.
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Extract code:hjln
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Extract code:k8ms Weights Download
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yolov3_COCO pretraining weights
Extract code:k8ms
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yolov4_COCO pretraining weights
Extract code:njdg