From 74f7cacf3910f34918f02d6153a81143614bad83 Mon Sep 17 00:00:00 2001 From: Wang Xin Date: Mon, 25 Jul 2022 14:40:30 +0800 Subject: [PATCH] update readme --- README.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index be03325..1d5a326 100644 --- a/README.md +++ b/README.md @@ -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/