---
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