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Jack Wang
Jack Wang

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Meet X-AnyLabeling: The Python-native, AI-powered Annotation Tool for Modern CV 🚀

#machinelearning #ai #deeplearning #computervision

The "Data Nightmare" 😱

Let’s be honest for a second.

As AI engineers, we love tweaking hyperparameters, designing architectures, and watching loss curves go down. But there is one part of the job that universally sucks: Data Labeling.

It’s the unglamorous bottleneck of every project. If you've ever spent a weekend manually drawing 2,000 bounding boxes on a dataset, you know the pain.

I realized the tooling landscape was broken:

  • Commercial SaaS: Great features, but expensive and I hate uploading sensitive data to the cloud.
  • Old-school OSS (LabelImg/Labelme): Simple, but "dumb." No AI assistance means 100% manual labor.
  • Heavy Web Suites (CVAT): Powerful, but requires a complex Docker deployment just to label a folder of images.

I wanted something different. I wanted a tool that felt like a lightweight desktop app but had the brain of a modern AI model.

X-AnyLabeling’s Vision

So, I built X-AnyLabeling. And today, we are releasing Version 3.0. 🎉

What is X-AnyLabeling? 🤖

X-AnyLabeling is a desktop-based data annotation tool built with Python and Qt. But unlike traditional tools, it’s designed to be "AI-First."

The philosophy is simple: Never label from scratch if a model can do a draft for you.

Whether you are doing Object Detection, Segmentation, Pose Estimation, or even Multimodal VQA, X-AnyLabeling lets you run a model (like YOLO, SAM, or Qwen-VL) to pre-label the data. You just verify and correct.

X-AnyLabeling Ecosystem

Here is what’s new in v3.0 and why it matters for developers.

1. Finally, a PyPI Package 📦

X-AnyLabeling Pypi

In the past, you had to clone the repo and pray the dependencies didn't break. We fixed that. You can now install the whole suite with a single command:

# Install with GPU support (CUDA 12.x)
pip install x-anylabeling-cvhub[cuda12]

# Or just the CPU version
pip install x-anylabeling-cvhub[cpu]
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We also added a CLI tool for those who love the terminal. Need to convert a dataset from COCO to YOLO format? Don't write a script; just run:

xanylabeling convert --task yolo2xlabel
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2. The "Remote Server" Architecture ☁️ -> 🖥️

X-AnyLabeling-Server

This is a big one for teams. Running a heavy model (like SAM-3 or a large VLM) on a annotator's laptop is slow or impossible.

We introduced X-AnyLabeling-Server, a lightweight FastAPI backend.

  • Server: You deploy the heavy models on a GPU machine.
  • Client: The annotator uses the lightweight UI on their laptop.
  • Result: Fast inference via REST API without local hardware constraints.

It supports custom models, Ollama, and Hugging Face Transformers out of the box.

3. The "Label-Train-Loop" with Ultralytics 🔄

Auto Training in X-AnyLabeling

We integrated the Ultralytics framework directly into the GUI.

You can now:

  1. Label a batch of images.
  2. Click "Train" inside the app.
  3. Wait for the YOLO model to finish training.
  4. Load that new model back into the app to auto-label the next batch of images.

This creates a positive feedback loop that drastically speeds up dataset creation.

4. Multimodal & Chatbot Capabilities 💬

Chatbot

Computer Vision isn't just boxes anymore. We added features for the LLM/VLM era:

  • VQA Mode: Structured annotation for document parsing or visual Q&A.
  • Chatbot: Connect to GPT-4, Gemini, or local models to "chat" with your images and auto-generate captions.
  • Export: One-click export to ShareGPT format for fine-tuning LLaMA-Factory models.

Supported Models (The "Batteries Included" List) 🔋

X-AnyLabeling's model zoo

We support 100+ models out of the box. You don't need to write inference code; just select them from the dropdown.

  • Segmentation: SAM 1/2/3, MobileSAM, EdgeSAM.
  • Detection: YOLOv5/8/10/11, RT-DETR, Gold-YOLO.
  • OCR: PP-OCRv5 (Great for multilingual text).
  • Multimodal: Qwen-VL, ChatGLM, GroundingDINO.

Try it out! 🛠️

This project is 100% Open Source.

We've hit 7.5k stars on GitHub, and we're just getting started. If you are tired of manual labeling or struggling with complex web-based annotation tools, give X-AnyLabeling a spin.

I’d love to hear your feedback in the comments! What features are you missing in your current data pipeline? 👇

Top comments (2)

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justin_nacu_7dd7db1c2fb81 profile image
Justin Nacu •

Ive been looking for something like this for a long time, been using labelme for a while but sometimes the workflow doesn't feel right to me.

X-AnyLabeling really ups my data labeling efficiency.

BTW, is there a guide for running SAM3 locally, for autoannotation with X-Anylabeling? because ive been running it on my Laptop for a while now its very good for autoannotation, but it takes a while to do inferences (My VRAM is not enough LOL, so Ive been running it on my CPU).

Thank you very much again for making this amazing tool.

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jack_wang_d47b1f7f781c64f profile image
Jack Wang •

Hi Justin,

Good news — the latest main branch now supports running SAM3 on CPU. Just run git pull origin main to update and try it out.

Details: github.com/CVHub520/X-AnyLabeling-...