---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---

# 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: