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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
|