WithoutBG Snap Models

Free, high-quality background removal models powered by AI

This repository contains the Snap tier models for the 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)

pip install withoutbg

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

from withoutbg import remove_background_batch

results = remove_background_batch([
    "image1.jpg", 
    "image2.jpg", 
    "image3.jpg"
], output_dir="results/")

Custom Model Paths

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

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
• Documentation: withoutbg.com/docs
• Demo: Hugging Face Space
• Commercial Licensing: 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
• Community: GitHub Discussions

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Try the models: Install pip install withoutbg and start removing backgrounds instantly!