This project implements an Image Classification model using ResNet-9 architecture to classify images from the CIFAR-10 dataset. The CIFAR-10 dataset consists of 60,000 32x32 color images across 10 classes, with 6,000 images per class.
This project focuses on building a deep learning model for image classification using the ResNet-9 architecture. ResNet-9 is a lightweight version of the ResNet architecture that is suitable for smaller datasets like CIFAR-10. The model achieves high accuracy by leveraging residual connections, which help mitigate the vanishing gradient problem in deep networks.
The CIFAR-10 dataset is a popular benchmark dataset for image classification tasks. It contains 60,000 32x32 color images divided into 10 classes:
- Airplane
- Automobile
- Bird
- Cat
- Deer
- Dog
- Frog
- Horse
- Ship
- Truck
The dataset is split into 50,000 training images and 10,000 test images.
The ResNet-9 architecture used in this project consists of:
- 9 convolutional layers
- Batch normalization
- ReLU activation functions
- Residual connections
- Fully connected layers for classification
This architecture is designed to perform well on small image datasets like CIFAR-10 while maintaining computational efficiency.
To run this project locally, follow these steps:
- Clone the repository:
git clone https://github.com/your-username/image-classification-resnet9-cifar10.git cd image-classification-resnet9-cifar10
After training, the model achieves an accuracy of around X% on the CIFAR-10 test set. Below are some example predictions:
| Image | Predicted Label |
|---|---|
 |
Airplane |
 |
Cat |
 |
Truck |
Contributions are welcome! If you find any issues or have suggestions, please feel free to open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for more details.