Update README.md

README.md

@@ -17,4 +17,74 @@ model-index:
           - name: Accuracy
             type: Accuracy
             value: 99.75
----
+---
+# MoE-CNN
+
+## Model Details
+
+### Model Description
+
+- **Model type:** Image Classification
+- **License:** MIT
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```
+model = MixtureOfExperts(num_experts=10)
+
+checkpoint_path = "FP_ML_MOE_SIMPLE_99_75.pth"
+checkpoint = torch.load(checkpoint_path)
+
+model.load_state_dict(checkpoint['model_state_dict'])
+
+print(f"Validation Accuracy: {checkpoint["val_accuracy"]:.2f}")
+```
+
+## Training Details
+
+### Training Data
+https://huggingface.co/datasets/ylecun/mnist
+
+### Training Procedure
+
+#### Data Augmentation
+- RandomRotation(10)
+- RandomAffine(0, shear=10)
+- RandomAffine(0, translate=(0.1, 0.1))
+- RandomResizedCrop(28, scale=(0.8, 1.0))
+- RandomPerspective(distortion_scale=0.2, p=0.5)
+- Resize((28, 28))
+
+#### Training Hyperparameters
+Adam with learning rate of 0.001 for fast initial convergence
+SGD with learning rate of 0.01 and learning rate decay to 0.001
+
+#### Size
+2,247,151 parameters with 674,145 effective parameters
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+https://huggingface.co/datasets/ylecun/mnist
+
+#### Metrics
+model-index:
+  - name: MoE-CNN
+    results:
+      - task:
+          type: image-classification
+        dataset:
+          name: MNIST
+          type: image-classification
+        metrics:
+          - name: Accuracy
+            type: Accuracy
+            value: 99.75
+## Technical Specifications [optional]
+
+### Model Architecture
+Mixture-of-Experts (MoE) architecture with a simple CNN as the experts.