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Features

Labelformat offers a robust set of features tailored to meet the diverse needs of computer vision engineers and data scientists.

Key Features

  • Wide Format Support:

    • 2D Object Detection: Bounding box annotations for object localization
    • Instance Segmentation: Pixel-level masks for precise object delineation

  • User-Friendly CLI and Python API:

    • CLI: Simple terminal commands to convert formats with customizable options.
    • Python API: Integrate label conversion seamlessly into your Python workflows.

  • Performance Optimizations:

    • Memory Conscious: Processes datasets file-by-file to minimize memory usage.
    • Minimal Dependencies: Targets Python 3.7 or higher, ensuring broad compatibility.

  • Cross-Platform Support:

    • Windows, Linux, and macOS: Works seamlessly across all major operating systems.

  • Reliability and Testing:

    • Typed Codebase: Ensures type safety and easier maintenance.
    • Round-Trip Tests: Guarantees label consistency across conversions.

  • Open-Source and Community-Driven:

    • MIT License: Free to use and modify.
    • Active Contributions: Regular updates and community support.

Supported Tasks and Formats

Object Detection

Why Labelformat?

Labelformat addresses the common challenges faced when dealing with diverse label formats:

  • Consistency: Ensures uniformity across different formats, crucial for model training.
  • Efficiency: Reduces the time spent on manual label format conversions.
  • Scalability: Handles large datasets with minimal memory footprint.
  • Flexibility: Supports a growing list of formats and tasks, adapting to evolving project needs.

Explore our Quick Start Guide to begin leveraging Labelformat's powerful features today!