cnn-cat-vs-dog-classifier

This project uses a convolutional neural network (CNN) with the EfficientNetB0 architecture to classify images of cats and dogs. The model is trained on the Dogs vs Cats dataset from Kaggle, and data augmentation techniques are applied to enhance performance and improve robustness.

Test accuracy obtained: 99.04%
Test loss obtained: 0.03

Dataset used:

https://www.kaggle.com/datasets/salader/dogs-vs-cats/data

How to Set Up Kaggle API

1. Go to your Kaggle account, and navigate to 'Account' settings.
2. Scroll down to the 'API' section and click 'Create New API Token'. A kaggle.json file will be downloaded.
3. Place the kaggle.json file in the appropriate directory:
   • For Linux or Mac: ~/.kaggle/kaggle.json
   • For Windows: C:\Users\<YourUsername>\.kaggle\kaggle.json
4. Run the following commands in the notebook to set up the Kaggle API: !mkdir -p ~/.kaggle !cp kaggle.json ~/.kaggle/ !chmod 600 ~/.kaggle/kaggle.json

Known Issues

• In the final output of the test evaluation, the printed text currently reads "Test accuracy and accuracy" instead of "Test accuracy and loss". This is a minor typo and does not affect the model's performance or results.