Update README with model details

README.md

@@ -1,2 +1,75 @@
-# SimpleCNN for CIFAR-10
-Model klasifikasi 10 kelas CIFAR-10 menggunakan CNN sederhana.
+---
+language: en
+tags:
+  - image-classification
+  - computer-vision
+  - pytorch
+  - cnn
+  - cifar10
+license: mit
+datasets:
+  - cifar10
+model-index:
+  - name: SimpleCNN CIFAR-10 Classifier
+    results: []
+---
+
+# 🧠 SimpleCNN CIFAR-10 Classifier
+
+📌 A simple Convolutional Neural Network (CNN) model trained on the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html), capable of recognizing 10 classes of common objects. The model was trained using PyTorch and is suitable for educational and prototyping purposes.
+
+## 🏷️ Classes
+
+- Airplane
+- Automobile
+- Bird
+- Cat
+- Deer
+- Dog
+- Frog
+- Horse
+- Ship
+- Truck
+
+## 🧰 Training Procedure
+
+1. Built a custom CNN model with 3 convolutional layers and 2 fully connected layers.
+2. Used MaxPooling after each conv layer and dropout for regularization.
+3. Resized all input images to 32x32 and applied normalization: `(mean=0.5, std=0.5)`.
+4. Training/validation split:
+   - 80% Training
+   - 20% Validation
+5. Training setup:
+   - Optimizer: Adam
+   - Loss Function: CrossEntropyLoss
+   - Batch size: 64
+   - Learning rate: 0.001
+   - Epochs: 10
+6. Saved the best-performing model as `pytorch_model.bin`.
+
+## 📊 Performance
+
+| Metric               | Value     |
+|----------------------|-----------|
+| Best Validation Accuracy | 88.76%    |
+
+## ⚙️ Framework & Environment
+
+- Python: 3.11
+- PyTorch: 2.x (Colab)
+- Torchvision: 0.15.x
+- Platform: Google Colab (GPU enabled)
+
+## 🧪 Hyperparameters
+
+| Parameter       | Value        |
+|-----------------|--------------|
+| Epochs          | 10           |
+| Batch Size      | 64           |
+| Optimizer       | Adam         |
+| Learning Rate   | 0.001        |
+| Loss Function   | CrossEntropy |
+| Image Size      | 32x32        |
+| Data Split      | 80% Train / 20% Val |
+
+---