  In this work, we build a series of machine learning models to predict the
price of a product given its image, and visualize the features that result in
higher or lower price predictions. We collect two novel datasets of product
images and their MSRP prices for this purpose: a bicycle dataset and a car
dataset. We set baselines for price regression using linear regression on
histogram of oriented gradients (HOG) and convolutional neural network (CNN)
features, and a baseline for price segment classification using a multiclass
SVM. For our main models, we train several deep CNNs using both transfer
learning and our own architectures, for both regression and classification. We
achieve strong results on both datasets, with deep CNNs significantly
outperforming other models in a variety of metrics. Finally, we use several
recently-developed methods to visualize the image features that result in
higher or lower prices.
