EfficientNet-B3 model for Tiny ImageNet classification, showcasing precision in a compact implementation.

This repository houses the implementation of EfficientNet-B3 for Tiny ImageNet classification. The project aims to explore the precision and efficiency of the EfficientNet-B3 architecture in the context of the scaled-down Tiny ImageNet dataset http://cs231n.stanford.edu/tiny-imagenet-200.zip .

• EfficientNet-B3 Model: Utilizes the state-of-the-art EfficientNet-B3 architecture for image classification.
• Compact Implementation: Demonstrates an efficient and concise implementation for Tiny ImageNet classification.
• Precision and Performance: Showcases the model's ability to achieve high precision with resource-efficient training.

• Python 3.x
• PyTorch
• Other dependencies (specified in requirements.txt)

1. Clone the repository:

   git clone https://github.com/yourusername/TinyImageNet-EfficientNet-B3.git
   cd TinyImageNet-EfficientNet-B3
   
   

pip install -r requirements.txt

Special thanks to Prof. Dr. Swarnendu Ghosh for his invaluable guidance and support throughout the development of this project.

This project is licensed under the MIT License. Feel free to customize the content based on specific details or additional features of your project.