🧠 SimpleCNN CIFAR-10 Classifier
📌 A simple Convolutional Neural Network (CNN) model trained on the CIFAR-10 dataset, 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
- Built a custom CNN model with 3 convolutional layers and 2 fully connected layers.
- Used MaxPooling after each conv layer and dropout for regularization.
- Resized all input images to 32x32 and applied normalization:
(mean=0.5, std=0.5).
- Training/validation split:
- 80% Training
- 20% Validation
- Training setup:
- Optimizer: Adam
- Loss Function: CrossEntropyLoss
- Batch size: 64
- Learning rate: 0.001
- Epochs: 10
- 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 |
