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 (ResNet-18)
+
+## Model Details
+- **Model Name**: `MRIResnet`
+- **Architecture**: ResNet-18-based model for MRI brain tumor classification  
+- **Dataset**: [Brain Tumor MRI Dataset](https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset)  
+- **Batch Size**: 32  
+- **Loss Function**: CrossEntropy Loss  
+- **Optimizer**: Adam (learning rate = 1e-3)  
+- **Transfer Learning**: Yes (pretrained ResNet-18 with modified layers)  
+
+## Model Architecture
+This model is based on **ResNet-18**, a widely used convolutional neural network, and has been adapted for **MRI-based brain tumor classification**. 
+
+### **Modifications**
+- **Input Channel Adaptation**: The first convolutional layer is modified to accept single-channel (grayscale) MRI scans.
+- **Classifier Head**: The fully connected (FC) layer is replaced to output 4 classes (assuming 4 tumor categories).
+- **Transfer Learning**:
+  - **Frozen Layers**: All pre-trained weights are frozen except for the modified layers.
+  - **Trainable Layers**:
+    - First convolutional layer (`conv1`)  
+    - Fully connected classification layer (`fc`)  
+
+## Implementation
+### **Model Definition**
+```python
+import torch
+import torch.nn as nn
+from torchvision.models import resnet18
+
+class MRIResnet(nn.Module, PyTorchModelHubMixin):
+    def __init__(self):
+        super().__init__()
+        self.base_model = resnet18(weights=True)
+        self.base_model.conv1 = nn.Conv2d(
+            1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
+        )
+        self.base_model.fc = nn.Linear(512, 4)
+
+        # Freeze all layers except the modified ones
+        for param in self.base_model.parameters():
+            param.requires_grad = False
+
+        for param in self.base_model.conv1.parameters():
+            param.requires_grad = True
+        for param in self.base_model.fc.parameters():
+            param.requires_grad = True
+
+    def forward(self, x):
+        return self.base_model(x)
+```
+
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: