File size: 1,013 Bytes
840031b f19576d 63fc00a f19576d fbaf7bd 1025eed fbaf7bd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
---
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
```
|