EfficientNet

EfficientNet implementation from scratch for classifying images from the Oxford-IIIT Pet Dataset

Project Structure

• efficientnet/: Contains the model and utility functions.
• data/: Data transformations.
• train.py: Script for training the model.
• evaluate.py: Script for evaluating the model.
• requirements.txt: List of dependencies.
• efficientnet_pet_model.pth: Pre-trained weights provided for your convenience.

Usage

1. Training the Model

To train the model, run:

python train.py

2. Evaluating the Model

First download your desired image and save it in the main directory of project with the name example.jpg

Then run the evaluate.py script to make predictions using the trained model:

python evaluate.py

Dependencies

Install the required dependencies using:

pip install -r requirements.txt

Dataset

The dataset used for training is the Oxford-IIIT Pet Dataset.

License

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

Acknowledgments

• The EfficientNet model architecture is inspired by the original EfficientNet paper.
• To implement the architecture, this post on Medium has been used.