---
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 |

---