The Dogs vs. Cats dataset is a standard computer vision dataset that involves classifying photos as either containing a dog or cat.
Although the problem sounds simple, it was only effectively addressed in the last few years using deep learning convolutional neural networks. While the dataset is effectively solved, it can be used as the basis for learning and practicing how to develop, evaluate, and use convolutional deep learning neural networks for image classification from scratch.
This includes how to develop a robust test harness for estimating the performance of the model, how to explore improvements to the model, and how to save the model and later load it to make predictions on new data.
This Project has been divided into six parts:
- Dogs vs. Cats Prediction Problem
- Dogs vs. Cats Dataset Preparation
- Develop a Baseline CNN Model
- Develop Model Improvements
- Explore Transfer Learning
- How to Finalize the Model and Make Predictions
The dataset can be downloaded for free from the Kaggle website: https://www.kaggle.com/c/dogs-vs-cats
- I have done this work in the Kaggle Notebook. The notebook provides us with free GPU that helps in Faster Coding.