---
metrics:
- accuracy: 0.9815
- Intersection Over Union: 0.9509
- Dice score: 0.9794
datasets:
- tangezerman/ain3007
base_model:
- 5mp-hub/vgg16.imagenet
---

# VGG16-U-NET Model Card

## Model Description

**Model Name:** VGG16-U-NET

**Model Type:** Image Segmentation

**Architecture:** U-Net with VGG16 weights trained on Imagenet


## Model Performance

| Metric | Value |
|--------|-------|
| Accuracy | 0.9815 |
| Intersection Over Union (IoU) | 0.9509 |
| Dice Score | 0.9794 |

## Training Details

**Dataset:** tangez/ain3007

**Training Parameters:**
- Architecture: U-Net with VGG16 encoder backbone
- Pre-trained weights: ImageNet
- Framework: PyTorch

## Intended Use

**Primary Use Cases:**
- Image segmentation tasks 

## How to Use

```python
import torch

# Load the trained model from the Models directory
model = torch.load("path/to/model.pth")
model.eval()

# For inference on WSI patches
with torch.no_grad():
    output = model(input_patches)
    # Output will be binary tissue masks
```