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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# WithoutBG Snap Models
+
+**Free, high-quality background removal models powered by AI**
+
+[![Apache 2.0 License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![ONNX Runtime](https://img.shields.io/badge/Runtime-ONNX-orange.svg)](https://onnxruntime.ai/)
+
+This repository contains the **Snap tier** models for the [withoutbg](https://github.com/withoutbg/withoutbg) library - a complete set of ONNX models for local, free background removal processing.
+
+## 📋 Model Overview
+
+The Snap tier implements a sophisticated 3-stage pipeline for background removal:
+
+| Model | File | Purpose | Input | Output | License |
+|-------|------|---------|-------|---------|---------|
+| **Depth Estimation** | `depth_anything_v2_vits_slim.onnx` | Stage 1: Depth map generation | RGB (518×518) | Inverse depth map | Apache 2.0 |
+| **Matting** | `snap_matting_0.1.0.onnx` | Stage 2: Initial background separation | RGBD (256×256) | Alpha channel (A1) | Apache 2.0 |
+| **Refiner** | `snap_refiner_0.1.0.onnx` | Stage 3: High-resolution refinement | RGB+D+A (original size) | Refined alpha (A2) | Apache 2.0 |
+
+## 🚀 Quick Start
+
+### Using the withoutbg library (Recommended)
+
+```bash
+pip install withoutbg
+```
+
+```python
+from withoutbg import remove_background
+
+# Automatically downloads and uses these models
+result = remove_background("image.jpg")
+result.save("output.png")
+```
+
+
+## 🔄 Processing Pipeline
+
+### Stage 1: Depth Estimation
+- **Model**: Depth Anything V2 ViT-S (Apache 2.0 licensed)
+- **Input**: RGB image (518×518 pixels, ImageNet normalized)
+- **Output**: Inverse depth map (0-255 range)
+- **Purpose**: Provides spatial understanding for better background separation
+
+### Stage 2: Matting
+- **Input**: RGBD (RGB + depth concatenated as 4-channel input, 256×256)
+- **Output**: Initial alpha channel (A1)
+- **Purpose**: Performs initial foreground/background segmentation
+
+### Stage 3: Refining
+- **Input**: RGB + depth + A1 (5-channel input at original resolution)
+- **Output**: Refined alpha channel (A2) with high detail
+- **Purpose**: Enhances edge quality and removes artifacts
+
+
+## 🔧 Technical Specifications
+
+### Model Details
+- **Framework**: ONNX (compatible with ONNX Runtime)
+- **Providers**: CPU, CUDA (automatically detected)
+- **Precision**: FP32
+- **Total Size**: ~140 MB (all three models)
+
+### Input Requirements
+- **Format**: RGB images (any resolution)
+- **Preprocessing**: Automatic resizing and normalization
+- **Output**: RGBA images with transparent background
+
+## 🏗️ Integration Examples
+
+### Batch Processing
+```python
+from withoutbg import remove_background_batch
+
+results = remove_background_batch([
+    "image1.jpg", 
+    "image2.jpg", 
+    "image3.jpg"
+], output_dir="results/")
+```
+
+### Custom Model Paths
+```python
+from withoutbg.models import SnapModel
+
+model = SnapModel(
+    depth_model_path="custom/depth_model.onnx",
+    matting_model_path="custom/matting_model.onnx", 
+    refiner_model_path="custom/refiner_model.onnx"
+)
+
+result = model.remove_background("image.jpg")
+```
+
+## 📄 Licensing
+
+### Open Source Components
+- **`depth_anything_v2_vits_slim.onnx`**: Apache 2.0 License
+  - Based on [Depth-Anything V2](https://github.com/DepthAnything/Depth-Anything-V2)
+  - Free for commercial and non-commercial use
+  - Source code available
+
+### Snap Tier Components  
+- **`snap_matting_0.1.0.onnx`**: Apache 2.0 License
+- **`snap_refiner_0.1.0.onnx`**: Apache 2.0 License
+- Free for commercial and non-commercial use
+- Open source models for the Snap tier
+
+## 🔗 Related Links
+
+- **Main Library**: [withoutbg/withoutbg](https://github.com/withoutbg/withoutbg)
+- **Documentation**: [withoutbg.com/docs](https://withoutbg.com/documentation)
+- **Demo**: [Hugging Face Space](https://huggingface.co/spaces/withoutbg/demo)
+- **Commercial Licensing**: [withoutbg.com/focus](https://withoutbg.com/focus)
+
+## 🎯 Use Cases
+
+- **Development & Prototyping**: Free local processing
+- **E-commerce**: Product photo background removal  
+- **Social Media**: Profile picture editing
+- **Content Creation**: Video thumbnails and graphics
+
+## 🤝 Contributing
+
+We welcome improvements to the open source components:
+
+1. **Depth Anything V2 optimizations**: Submit PRs to improve inference speed
+2. **Preprocessing enhancements**: Better image handling and normalization
+3. **Documentation**: Examples, tutorials, and integration guides
+4. **Bug reports**: Issues with model loading or inference
+
+## 📧 Support
+
+- **Technical Issues**: [GitHub Issues](https://github.com/withoutbg/withoutbg/issues)
+- **Community**: [GitHub Discussions](https://github.com/withoutbg/withoutbg/discussions)
+
+---
+
+**Try the models**: Install `pip install withoutbg` and start removing backgrounds instantly!