Update README.md

README.md

@@ -4,7 +4,55 @@ metrics:
 - Intersection Over Union: 0.9509
 - Dice score: 0.9794
 datasets:
-- tangez/ain3007
+- tangez/zin5807
 base_model:
-- smp-hub/vgg16.imagenet
----
+- 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 encoder
+
+
+## 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
+```