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---
title: Background Removal Tool
emoji: 👀
colorFrom: blue
colorTo: blue
sdk: gradio
sdk_version: 5.12.0
app_file: app.py
pinned: false
short_description: A tool to remove image backgrounds with precision
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
# Background Removal Tool
This is a deep learning-powered **Background Removal Tool** that uses image segmentation models to remove backgrounds from images and add transparency (alpha channel). It features a user-friendly interface built with Gradio to interact with the tool via image uploads, URLs, or file outputs.
---
## Features
1. **Two Segmentation Models**:
- `BiRefNet`: Efficient and robust segmentation model.
- `RMBG-2.0`: Advanced model for refined background removal.
2. **Multiple Input Methods**:
- Upload images directly from your system.
- Provide an image URL for processing.
- Upload and save the processed image as a PNG file with transparency.
3. **Customizable**: Switch between models for different use cases.
4. **Fast and GPU-Powered**: Leverages CUDA for faster processing on GPUs.
---
## Requirements
- Python 3.8+
- A GPU-enabled environment for CUDA support (optional but recommended).
- Installed Python libraries:
- `gradio`
- `torch`
- `transformers`
- `torchvision`
- `Pillow`
- `numpy`
Install dependencies using:
```bash
pip install gradio torch torchvision transformers Pillow numpy
```
---
## Usage
### Run the Application
Execute the script using:
```bash
python inference.py
```
### Interface
#### Tab 1: Image Upload
1. Upload an image from your local system.
2. Select a model (`BiRefNet` or `RMBG-2.0`).
3. View and download the processed image with the background removed.
#### Tab 2: URL Input
1. Paste the URL of an image.
2. Select a model (`BiRefNet` or `RMBG-2.0`).
3. View and download the processed image with the background removed.
#### Tab 3: File Output
1. Upload an image file.
2. Select a model (`BiRefNet` or `RMBG-2.0`).
3. Get the path to the processed PNG file with transparency.
### Example
- Use the provided example image (`ironman.jpg`) to test the tool.
---
## How It Works
1. **Model Loading**:
- Loads pre-trained segmentation models from Hugging Face.
2. **Image Preprocessing**:
- Resizes and normalizes the input image.
3. **Background Removal**:
- The model generates a mask for the image background.
- The mask is applied to create a transparent background.
4. **Output**:
- Processed image is displayed or saved with an alpha channel.
---
## Contributing
Feel free to submit issues or pull requests for improvements or bug fixes.
---
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