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