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README.md

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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