README_HF.md

metadata

title: carioLabel - Image Annotation Tool
emoji: 🎯
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit

🎯 carioLabel - Web Edition

Browser-based Image Annotation Tool with YOLO Training

Features

• 📦 Bounding Box Annotation - Click to add boxes on images
• 🏷️ Label Management - Organize and categorize annotations
• 💾 Multi-Format Export - YOLO, Pascal VOC formats
• 🚀 GPU Training - Train YOLO models directly in browser
• 🌐 Zero Installation - Works in any modern browser
• 🆓 Free to Use - Open source and community-driven

How to Use

1. Annotation

1. Upload an image
2. Enter a label name (e.g., "cat", "dog", "person")
3. Click on the image to add bounding boxes
4. Repeat for multiple objects

2. Export

• Choose format: YOLO or Pascal VOC
• Click "Export Annotations"
• Copy the output for your dataset

3. Training (Optional)

• Upload your dataset as ZIP
• Configure epochs and batch size
• Click "Start Training"
• Wait for results (uses HF GPU!)

Tech Stack

• Gradio - Web UI framework
• Ultralytics YOLO - Object detection
• PyTorch - Deep learning backend
• OpenCV - Image processing

Local Development

# Clone repo
git clone https://huggingface.co/spaces/YOUR_USERNAME/carioLabel

# Install dependencies
pip install -r requirements.txt

# Run locally
python app.py

Deployment

This Space is automatically deployed to Hugging Face Spaces.

Just push your changes:

git add .
git commit -m "Update app"
git push

Limitations

• Demo version has simplified box creation (click-based, not drag)
• Training requires properly formatted dataset
• Large datasets may hit memory limits

For production use with advanced features, see the desktop version.

Credits

Author: CareIO AI
Contact: herdodimas46@gmail.com
Website: https://www.careio.my.id
Instagram: @careio.ai

License

MIT License - Free to use for personal and commercial projects

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Note: This is a web-based demo. For advanced features and offline use, check out the full desktop application.