CIFAR10 Classification with PyTorch

This project uses the CIFAR10 dataset for training. It consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class. (Source:https://www.cs.toronto.edu/~kriz/cifar.html)

Steps:

• Exploring the dataset
• Defining a neural network architecture
• Hyper-parameter search and training the model
  • without data augmentation
  • with data augmentation
  • with VGG16 (transfer learning)
• Evaluating the model's results and making cool graphs!

Results:

	Train Accuracy	Test Accuracy
without data augmentation	*81.69%	76.68%
with data augmentation	85.15%	79.76%
with transfer learning (VGG-16)	92.89%	85.93%

_{* - running accuracy}