Update README.md

README.md

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 - pytorch_model_hub_mixin
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+# Model Card: MRI Brain Tumor Classification Model
+
+## Model Details
+- **Architecture**: EfficientNet-B1-based MRI classification model  
+- **Dataset**: [Brain Tumor MRI Dataset](https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset)  
+- **Batch Size**: 32  
+- **Loss Function**: Triplet Margin Loss with Cosine Similarity  
+- **Optimizer**: Adam (learning rate = 1e-2)  
+
+## Model Architecture
+This model is based on **EfficientNet-B1** and has been modified for MRI brain tumor classification. The main adaptations include:  
+
+### **Modifications**:
+- **Input Channel Adjustment**: The first convolutional layer is changed to accept single-channel (grayscale) MRI scans.
+- **Classifier Head**: The default classifier is replaced with a custom MLP featuring:
+  - Fully connected layers with 1280 → 756 → 256 units.
+  - SiLU activation.
+  - Batch normalization.
+  - Dropout for regularization.
+
+### **Triplet Loss for Metric Learning**:
+The model uses **Triplet Margin Loss** with **Cosine Similarity** to learn an embedding space where MRI images of the same class are closer together, while images from different classes are farther apart.
+
+## Implementation
+### **Model Definition**
+```python
+import torch
+import torch.nn as nn
+from torchvision.models import efficientnet_b1
+from torch.nn import TripletMarginWithDistanceLoss
+from torch.nn.functional import cosine_similarity
+
+class MRIModel(nn.Module, PyTorchModelHubMixin):
+    def __init__(self):
+        super(MRIModel, self).__init__()
+        self.base_model = efficientnet_b1(weights=False)
+        self.base_model.features[0] = nn.Sequential(
+            nn.Conv2d(1, 32, kernel_size=(3, 3), stride=(2, 2), bias=False),
+            nn.BatchNorm2d(32),
+            nn.ReLU6(inplace=True),
+        )
+        self.base_model.classifier = nn.Sequential(
+            nn.Linear(1280, 756),
+            nn.SiLU(),
+            nn.BatchNorm1d(756),
+            nn.Dropout(0.2),
+            nn.Linear(756, 256),
+        )
+
+    def forward(self, x):
+        return self.base_model(x)
+```
+
+## Training Configuration
+- Batch Size: 32
+- Loss Function: Triplet Margin Loss (Cosine Similarity)
+- Optimizer: Adam (learning rate = 1e-2)
+
+
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: