| --- |
| license: mit |
| library_name: pytorch |
| tags: |
| - background-removal |
| - image-segmentation |
| - computer-vision |
| - pytorch |
| - foreground-extraction |
| pipeline_tag: image-segmentation |
| --- |
| |
| # Background Remover (BEN2 Base) |
|
|
| **BEN2 Base** is a deep learning model for **automatic background removal** from images. |
| The model predicts a **foreground segmentation mask** that can be used to remove or replace the background. |
|
|
| This repository contains the pretrained weights: |
|
|
| `BEN2_Base.pth` |
|
|
| The model can be used in: |
|
|
| - photo editing tools |
| - product image processing |
| - portrait segmentation |
| - dataset preprocessing |
| - AI image pipelines |
|
|
| --- |
|
|
| # Model Details |
|
|
| | Property | Value | |
| |--------|--------| |
| | Model Name | BEN2 Base | |
| | Task | Background Removal | |
| | Architecture | Segmentation Network | |
| | Framework | PyTorch | |
| | File Size | 1.13 GB | |
| | Input | RGB image | |
| | Output | Foreground mask | |
|
|
| --- |
|
|
| # Repository Files |
|
|
| | File | Description | |
| |-----|-------------| |
| | BEN2_Base.pth | Pretrained background removal model weights | |
| |
| --- |
| |
| # Installation |
| |
| Install required libraries: |
| |
| ```bash |
| pip install torch torchvision pillow numpy opencv-python |
| ``` |
| |
| --- |
| |
| # Usage Example |
| |
| Example inference using PyTorch. |
| |
| ```python |
| import torch |
| from PIL import Image |
| import torchvision.transforms as transforms |
| |
| # Load model |
| model = torch.load("BEN2_Base.pth", map_location="cpu") |
| model.eval() |
| |
| # Preprocessing |
| transform = transforms.Compose([ |
| transforms.Resize((512, 512)), |
| transforms.ToTensor() |
| ]) |
| |
| image = Image.open("input.jpg").convert("RGB") |
| input_tensor = transform(image).unsqueeze(0) |
|
|
| # Inference |
| with torch.no_grad(): |
| output = model(input_tensor) |
|
|
| mask = output.squeeze().cpu().numpy() |
| ``` |
| |
| You can apply the mask to generate a **transparent PNG** or replace the background. |
| |
| --- |
| |
| # Example Workflow |
| |
| 1. Load an image |
| 2. Resize and normalize |
| 3. Run model inference |
| 4. Generate segmentation mask |
| 5. Remove background |
| |
| --- |
| |
| # Use Cases |
| |
| ### E-commerce |
| Remove backgrounds from product images. |
| |
| ### Portrait Editing |
| Create clean profile images. |
| |
| ### Content Creation |
| Prepare images for thumbnails, ads, or designs. |
| |
| ### AI Pipelines |
| Preprocess images for ML datasets. |
| |
| --- |
| |
| # Limitations |
| |
| - Performance may vary with extremely complex backgrounds. |
| - Very small foreground objects may reduce segmentation quality. |
| - Images should be resized for optimal results. |
| |
| --- |
| |
| # Training |
| |
| This repository provides **pretrained weights only**. |
| |
| --- |
| |
| # License |
| |
| Please verify the license before using the model in commercial applications. |
| |
| --- |
| |
| # Author |
| |
| Ashank Gupta |
| |
