CNN Model for CIFAR-10 Classification

Project Overview

This project involves training a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset. The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 different classes, with 6,000 images per class. The classes are: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.

Model Architecture

The CNN model is designed with multiple convolutional layers, followed by max-pooling layers, and fully connected layers. The architecture is as follows:

1. Convolutional Layer 1: 32 filters, 3x3 kernel, ReLU activation
2. Max Pooling Layer 1: 2x2 pool size
3. Convolutional Layer 2: 64 filters, 3x3 kernel, ReLU activation
4. Max Pooling Layer 2: 2x2 pool size
5. Fully Connected Layer 1: 512 units, ReLU activation
6. Output Layer: 10 units, Softmax activation

class Cifar10CnnModel(ImageClassificationBase):
    def __init__(self):
        super().__init__()
        self.network = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2, 2), # output: 64 x 16 x 16

            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2, 2), # output: 128 x 8 x 8

            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2, 2), # output: 256 x 4 x 4

            nn.Flatten(),
            nn.Linear(256*4*4, 1024), # activation layer 1
            nn.ReLU(),
            nn.Linear(1024, 512), # activation layer 2
            nn.ReLU(),
            nn.Linear(512, 10)) # activation layer 3

    def forward(self, xb):
        return self.network(xb)

Benchmarks - 75% Accuracy

CNN Kernel overview

2x2 Max-Pool diagram

Conv2d

Dataset

The CIFAR-10 dataset is used, which can be downloaded from the official website.

Training

The model is trained using the following parameters:

• Optimizer: Adam
• Loss Function: Categorical Crossentropy
• Batch Size: 128

Installation

1. Clone the repository:

git clone https://github.com/swarajkumarsingh/cnn-cifar-classification-model.git

2. Open Google Collab and open the model file

Results

The trained model achieves an accuracy of approximately 75% on the test set.

Contributing

Contributions are welcome! Please fork the repository and create a pull request with your changes.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

• The CIFAR-10 dataset creators
• Pytorch and nn Module