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Rahuletto/
CNN

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CNN with CIFAR-10

A PyTorch implementation of a Convolutional Neural Network (CNN) for image classification on the CIFAR-10 dataset, achieving 81.45% test accuracy.

Try it out! https://huggingface.co/spaces/Rahuletto/CNN

Architecture

The CNN model consists of

Convolutional Layers:

Conv1: 3 → 32 channels, 3x3 kernel, padding=1
Conv2: 32 → 64 channels, 3x3 kernel, padding=1
Conv3: 64 → 128 channels, 3x3 kernel, padding=1

Others

Batch Normalization after each convolutional layer
MaxPooling2D (2x2) for downsampling
ReLU activation functions
Fully Connected Layers: 2048 → 512 → 10
Dropout (50%) for regularization

Getting Started

Prerequisites

Python 3.12+
PyTorch 2.7.1+
torchvision 0.22.1+

[!TIP] This project was developed with uv, so it is best to use uv for project management.

Installation

Clone the repository:

bash
1git clone https://github.com/rahuletto/cnn 2cd CNN
Create virtual environment:

bash
1python -m venv .venv 2source .venv/bin/activate # Windows: .venv\Scripts\activate
Install dependencies:

pip install -r requirements.txt

Running the model

Run the main file

python main.py

You can play around in the gradio interface

https://github.com/user-attachments/assets/1f742c32-79bd-4d16-a74f-68c241f4a841

Model Code

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, stride=1, padding=1)  # 32x32 -> 16x16
        self.bn1 = nn.BatchNorm2d(32)
        self.conv2 = nn.Conv2d(32, 64, 3, stride=1, padding=1)  # 16x16 -> 8x8
        self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 128, 3, stride=1, padding=1)  # 8x8 -> 4x4
        self.bn3 = nn.BatchNorm2d(128)
        self.pool = nn.MaxPool2d(stride=2, kernel_size=2)
        self.fc1 = nn.Linear(128 * 4 * 4, 512)
        self.fc2 = nn.Linear(512, 10)
        self.dropout = nn.Dropout(0.5)

    def forward(self, x):
        x = self.pool(F.relu(self.bn1(self.conv1(x))))
        x = self.pool(F.relu(self.bn2(self.conv2(x))))
        x = self.pool(F.relu(self.bn3(self.conv3(x))))
        x = x.view(x.size(0), -1)
        x = self.dropout(x)
        x = F.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return x

Training Configuration

Optimizer: Adam (lr=0.001)
Batch Size: 64
Epochs: 50

Best model checkpoint was saved at epoch 49 with validation loss of 0.6553.

Model

There are two CNN models in cnn/ folder

model.pt
model-old.pt

model.pt was trained with BatchNorm2d to reach 81.45% accuracy in CIFAR-10 dataset model-old.pt was trained without fine tuning which gets 75% accuracy in CIFAR-10 dataset

Accuracy:

Total Accuracy: 81.45%

Airplane: 84.60%
Automobile: 93.20%
Bird: 76.90%
Cat: 69.70%
Deer: 77.20%
Dog: 64.00%
Frog: 89.30%
Horse: 82.10%
Ship: 89.60%
Truck: 87.90%

References

License

This project is licensed under the MIT License - see the LICENSE file for details.