8 Image classification
This chapter covers
- Understanding convolutional neural networks (convnets)
- Using data augmentation to mitigate overfitting
- Using a pretrained convnet for feature extraction
- Fine-tuning a pretrained convnet
Computer vision was the first big success story of deep learning. It led to the initial rise of deep learning between 2011 and 2015. A type of deep learning called convolutional neural networks started getting remarkably good results on image classification competitions around that time, first with Dan Ciresan winning two niche competitions (the ICDAR 2011 Chinese character recognition competition and the IJCNN 2011 German traffic signs recognition competition), then more notably in Fall 2012 with Hinton’s group winning the high-profile ImageNet large-scale visual recognition challenge. Many more promising results quickly started bubbling up in other computer vision tasks.