metadata
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
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