labelme-to-yolo

Convert LabelMe Annotation Format to YOLO Annotation Format for Segmentation

https://github.com/tlaloc-es/labelme-to-yolo

Science Score: 44.0%

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  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
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  • Scientific vocabulary similarity
    Low similarity (8.6%) to scientific vocabulary

Keywords

dataset labelme labelme-annotations ultraly yolo
Last synced: 7 months ago · JSON representation ·

Repository

Convert LabelMe Annotation Format to YOLO Annotation Format for Segmentation

Basic Info
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
dataset labelme labelme-annotations ultraly yolo
Created about 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme Changelog Citation

README.md

LabelMe to Yolo

[![Downloads](https://static.pepy.tech/personalized-badge/labelme-to-yolo?period=month&units=international_system&left_color=grey&right_color=blue&left_text=PyPi%20Downloads)](https://pepy.tech/project/labelme-to-yolo) [![Stars](https://img.shields.io/github/stars/Tlaloc-Es/labelme-to-yolo?color=yellow&style=flat)](https://github.com/Tlaloc-Es/labelme-to-yolo/stargazers)

Convert LabelMe format into Ultralytics Yolo format for instance segmentation.

Installation PyPI

You can install labelme-to-yolo from Pypi. It's going to install the library itself and its prerequisites as well.

bash pip install labelme2yolo

You can install labelme2yolo from its source code.

bash git clone https://github.com/Tlaloc-Es/labelme-to-yolo.git cd labelme2yolo pip install -e .

Usage

First of all, make your dataset with LabelMe, after that call to the following command

labelme2yolo --source-path /labelme/dataset --output-path /another/path

The arguments are:

  • --source-path: That indicates the path where are the json output of LabelMe and their images, both will have been in the same folder
  • --output-path: The path where you will save the converted files and a copy of the images following the yolov7 folder estructure

Expected output

If you execute the following command:

labelme2yolo --source-path /labelme/dataset --output-path /another/datasets

You will get something like this

bash datasets ├── images │ ├── train │ │ ├── img_1.jpg │ │ ├── img_2.jpg │ │ ├── img_3.jpg │ │ ├── img_4.jpg │ │ └── img_5.jpg │ └── val │ ├── img_6.jpg │ └── img_7.jpg ├── labels │ ├── train │ │ ├── img_1.txt │ │ ├── img_2.txt │ │ ├── img_3.txt │ │ ├── img_4.txt │ │ └── img_5.txt │ └── val │ ├── img_6.txt │ └── img_7.txt ├── labels.txt ├── test.txt ├── train.txt └── project.yml

Donation

If you want to contribute you can make a donation at https://www.buymeacoffee.com/tlaloc, thanks in advance

Owner

  • Name: Tlaloc-Es
  • Login: Tlaloc-Es
  • Kind: user

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Tlaloc-Es
title: "labelme-to-yolo"
version: 0.1.0
date-released: 2024-29-12

GitHub Events

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Last Year
  • Push event: 6
  • Create event: 1

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 33 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
  • Total maintainers: 1
pypi.org: labelme-to-yolo

A tool to convert LabelMe dataset annotations into YOLO format for instance segmentation.

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 33 Last month
Rankings
Dependent packages count: 9.8%
Average: 32.6%
Dependent repos count: 55.3%
Maintainers (1)
Last synced: 8 months ago

Dependencies

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