LabelBee-Client

Features

📦 Out of the Box, built-in six annotation tools, simple configurations
🪵 Flexible combinations, multiple tools can directly rely on each other
💻 Multiple operating systems: Mac / Linux / Windows
🏁 Support Data Formats

	General Data	COCO	Semantic Segmentation Mask
Export	✔️	✔️	✔️
Import	✔️	✖	✖

Download

Mac & Windows & Linux

Support Scenes

Detection: Detection scenes for vehicles, license plates, pedestrians, faces, industrial parts, etc.
Classification: Detection of object classification, target characteristics, right and wrong judgments, and other classification scenarios
Semantic segmentation: Human body segmentation, panoramic segmentation, drivable area segmentation, vehicle segmentation, etc.
Text transcription: Text detection and recognition of license plates, invoices, insurance policies, signs, etc.
Contour detection: positioning line scenes such as human contour lines, lane lines, etc.
Key point detection: positioning scenes such as human face key points, vehicle key points, road edge key points, etc.

Detection / Segmentation

Line / Point / Text

Usage

Getting Started

Annotation Format

{
  "width": 4368,
  "height": 2912,
  "valid": true,
  "rotate": 0,
  "step_1": {
    "toolName": "rectTool",
    "result": [
      {
        "x": 530.7826086956522,
        "y": 1149.217391304348,
        "width": 1314.7826086956522,
        "height": 1655.6521739130435,
        "attribute": "",
        "valid": true,
        "id": "Rp1x6bZs",
        "sourceID": "",
        "textAttribute": "",
        "order": 1
      }
    ]
  }
}

For details, click to view LabelBee Annotation Format

Important

js can't export 8bit unsign Int image, so a python script is provided to solve this problem

[Script] (https://github.com/open-mmlab/labelbee-client/blob/main/scripts/dataTransfer.py)

LICENSE

This project is released under the Apache 2.0 license.

open-mmlab / labelbee-client

readme