Labelme2YOLO

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Labelme2YOLO efficiently converts LabelMe's JSON format to the YOLOv5 dataset format. It also supports YOLOv5/YOLOv8 segmentation datasets, making it simple to convert existing LabelMe segmentation datasets to YOLO format.

New Features

export data as yolo polygon annotation (for YOLOv5 & YOLOV8 segmentation)
Now you can choose the output format of the label text. The two available alternatives are polygon and bounding box(bbox).

Performance

Labelme2YOLO is implemented in Rust, which makes it significantly faster than equivalent Python implementations. In fact, it can be up to 100 times faster, allowing you to process large datasets more efficiently.

Installation

pip install labelme2yolo

Arguments

[LABEL_LIST]... Comma-separated list of labels in the dataset.

Options

-d, --json_dir Directory containing LabelMe JSON files.

--val_size Proportion of the dataset to use for validation (between 0.0 and 1.0) [default: 0.2].

--test_size Proportion of the dataset to use for testing (between 0.0 and 1.0) [default: 0].

--output_format Output format for YOLO annotations: 'bbox' or 'polygon' [default: bbox] [aliases: format] [possible values: polygon, bbox].

--seed Seed for random shuffling [default: 42].

-h, --help Print help.

-V, --version Print version.

How to Use

1. Converting JSON files and splitting training, validation datasets

You may need to place all LabelMe JSON files under labelme_json_dir and then run the following command:

labelme2yolo --json_dir /path/to/labelme_json_dir/

This tool will generate dataset labels and images with YOLO format in different folders, such as

/path/to/labelme_json_dir/YOLODataset/labels/train/
/path/to/labelme_json_dir/YOLODataset/labels/val/
/path/to/labelme_json_dir/YOLODataset/images/train/
/path/to/labelme_json_dir/YOLODataset/images/val/
/path/to/labelme_json_dir/YOLODataset/dataset.yaml

2. Converting JSON files and splitting training, validation, and test datasets with --val_size and --test_size