Mini-Vision-V1: CIFAR-10 CNN Classifier

Welcome to Mini-Vision-V1, the first model in the Mini-Vision series. This project demonstrates a robust implementation of a Convolutional Neural Network (CNN) for image classification using the CIFAR-10 dataset. It is designed to be lightweight, efficient, and easy to understand, making it perfect for beginners learning PyTorch.

Model Description

Mini-Vision-V1 is a custom 4-layer CNN architecture. It utilizes Batch Normalization and Dropout to prevent overfitting and ensure stable training. With only 1.34M parameters, it achieves a competitive accuracy on the CIFAR-10 test set.

• Dataset: CIFAR-10 (32x32 color images, 10 classes)
• Framework: PyTorch
• Total Parameters: 1.34M

Model Architecture

The network consists of 4 convolutional blocks followed by a classifier head.

Layer	Input Channels	Output Channels	Kernel Size	Stride	Padding	Activation	Other
Conv Block 1	3	32	5	1	2	ReLU	MaxPool(2), BatchNorm
Conv Block 2	32	64	5	1	2	ReLU	MaxPool(2), BatchNorm
Conv Block 3	64	128	5	1	2	ReLU	MaxPool(2), BatchNorm
Conv Block 4	128	256	5	1	2	ReLU	MaxPool(2), BatchNorm
Flatten	-	-	-	-	-	-	Output: 1024
Linear 1	1024	256	-	-	-	ReLU	Dropout(0.5)
Linear 2	256	10	-	-	-	-	-

Training Strategy

The model was trained using standard practices for CIFAR-10 to maximize performance on a small footprint.

• Optimizer: SGD (Momentum=0.9)
• Initial Learning Rate: 0.007
• Scheduler: StepLR (Step size=5, Gamma=0.5)
• Loss Function: CrossEntropyLoss
• Batch Size: 256
• Epochs: Total 100 epochs, Best Accuracy 31 epoch
• Data Augmentation:
  • Random Crop (32x32 with padding=4)
  • Random Horizontal Flip

Performance

The model achieved the following results on the CIFAR-10 test set:

Metric	Value
Test Accuracy	78%
Parameters	1.34M

Training Visualization (TensorBoard)

Below are the training and testing curves visualized via TensorBoard.

1. Training Loss

(Recorded every step)

2. Test Loss

(Recorded every epoch)

Quick Start

Dependencies

• Python 3.x
• PyTorch
• Torchvision
• requirements.txt

Inference

You can easily load the model and perform inference on a single image using the test.py file.

File Structure

.
├── model.py               # Model architecture definition
├── train.py               # Training script
├── test.py                # Inference script
├── Mini-Vision-V1.pth     # Trained model weights
├── config.json
├── README.md
└── assets
      ├── train_loss.png   # Visualized train loss graph
      └── test_loss.png    # Visualized test loss graph

License

This project is licensed under the MIT License.