This is an image classifier for the well-known CIFAR-10 dataset. The dataset contains 50,000 train data and 10,000 test data of 32x32 RGB colour images of 10 categories of objects. This project compares the performance and efficiency of several deep learning architectures of Multi-Layer Perceptron, LeNet CNN and 3 block VGG.
The best model is the Optimised VGG-3 block with 85% accuracy & quick convergence while LeNet offers the best efficiency (in terms of accuracy per unit of training time) and achieved 69% accuracy in just about one and half minutes.
The code is written in Python and executed either on a Jupyter Notebook or Google Colab.
