CNN-pytorch
Collection of CNN models and implementations
Fashion-MNIST
CNN implementation for predicting items of clothing
Summary
Using a relatively simple CNN model for image classicification.
MNIST data can be found here or in this case from torchvision
Findings
Classification results of CNN model trained on 15 epochs, final test accuracy of 88.84%
CIFAR-10
CNN implementation for predicting objects/animals
Summary
Using a larger CNN model with batch normalisation for image classicification.
Data augmentation of the image was necessary for training, see here for more information.
Example of data Augmentation used in final model
transformer_train = torchvision.transforms.Compose([
transforms.RandomCrop(32, padding=4),
torchvision.transforms.RandomHorizontalFlip(p=0.5),
torchvision.transforms.RandomAffine(0, translate=(0.1, 0.1)),
transforms.ToTensor(),
])
CIFAR data can be found here or in this case from torchvision
Findings
Classification results of CNN model trained on 30 epochs, final test accuracy of 84.96%
Food-5k
CNN implementation for predicting whether the image is food
Summary
Using transfer learning with a VGG model, modified for image classification.
Data augmentation of the image was also used, see here for more information.
Food-5k data can be found here
Findings
Classification results of the VGG model trained on 5 epochs, final test accuracy of 98.40%