README.md

metadata

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.

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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.

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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:

pip install gradio torch torchvision transformers Pillow numpy

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Usage

Run the Application

Execute the script using:

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.

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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.

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Contributing

Feel free to submit issues or pull requests for improvements or bug fixes.

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