CVHub520/X-AnyLabeling-Server

A Simple, Lightweight, and Extensible Serving Framework for X-AnyLabeling

X‑AnyLabeling‑Server – What It Is

X‑AnyLabeling‑Server is a lightweight, extensible serving framework that lets you run AI models behind the X‑AnyLabeling desktop annotation tool. It focuses on the service side: starting a server, handling requests, scheduling resources, logging, authentication, etc., while keeping the actual model code separate. In short, it turns a model into a production‑ready API that the labeling UI can call for auto‑labeling.

Key Features (as described in the README)

Feature What It Means for a User
Decoupled design The server manages lifecycle and resources; you don’t have to modify model code to fit the framework.
Pluggable architecture New models can be added by dropping in a small plug‑in (e.g., a Python class) without touching the core server code.
Production‑ready Built‑in structured logging, error handling, concurrency limits, and security/authentication out of the box.
Flexible configuration All settings (ports, model paths, hardware resources, etc.) are in a config file with sensible defaults, making it easy to adapt to different deployment environments (Linux, Windows, macOS).

How It Works (high‑level flow)

  1. Configure the server via the provided YAML/JSON config files (see the Configuration Guide).
  2. Plug in your model by implementing the required interface (see the Custom Model Integration guide).
  3. Start the server – it launches a FastAPI/Starlette‑based HTTP service (the README links to an OpenAPI schema).
  4. The X‑AnyLabeling desktop app sends inference requests (e.g., image, video frames) to the server’s API.
  5. The server runs the model, returns predictions, and logs the request for later analysis.

Who Might Use It

  • Data‑annotation teams that need automated labeling (object detection, segmentation, etc.) integrated directly into their annotation UI.
  • ML engineers looking for a quick way to expose a model as a REST service without writing boilerplate server code.
  • Researchers who want a reproducible, configurable inference service for experiments on auto‑labeling pipelines.

Getting Started

The repository includes a docs folder with step‑by‑step guides:

  • Installation & setup
  • Detailed configuration options
  • How to add a custom model
  • API reference (router guide) and an OpenAPI JSON file for client generation.

Community & Contribution

The project welcomes contributions (see the CONTRIBUTING.md). Sponsorship options are listed (Ko‑fi, WeChat/Alipay). A citation block is provided for academic use.

Bottom Line

X‑AnyLabeling‑Server is a focused, production‑oriented serving layer that makes it easy to attach AI inference models to the X‑AnyLabeling annotation platform, handling the usual server concerns while staying lightweight and extensible.

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