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update readme

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Wang Xin 2022-07-25 14:40:30 +08:00
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@ -6,23 +6,25 @@ If you've already marked your segmentation dataset by LabelMe, it's easy to use
## Parameters Explain
**--json_dir** LabelMe JSON files folder path.
**--val_size (Optional)** Validation dataset size, for example 0.2 means 20% for validation and 80% for training.
**--val_size (Optional)** Validation dataset size, for example 0.2 means 20% for validation.
**--test_size (Optional)** Validation dataset size, for example 0.2 means 20% for validation and 80% for training.
**--test_size (Optional)** Test dataset size, for example 0.2 means 20% for Test.
**--json_name (Optional)** Convert single LabelMe JSON file.
## How to Use
### 1. Convert JSON files, split training and validation dataset by --val_size
### 1. Convert JSON files, split training, validation and test dataset by --val_size and --test_size
Put all LabelMe JSON files under **labelme_json_dir**, and run this python command.
```bash
python labelme2yolo.py --json_dir /home/username/labelme_json_dir/ --val_size 0.2
python labelme2yolo.py --json_dir /home/username/labelme_json_dir/ --val_size 0.15 --test_size 0.15
```
Script would generate YOLO format dataset labels and images under different folders, for example,
```bash
/home/username/labelme_json_dir/YOLODataset/labels/train/
/home/username/labelme_json_dir/YOLODataset/labels/test/
/home/username/labelme_json_dir/YOLODataset/labels/val/
/home/username/labelme_json_dir/YOLODataset/images/test/
/home/username/labelme_json_dir/YOLODataset/images/train/
/home/username/labelme_json_dir/YOLODataset/images/val/