NFT classification using Computer Vision is a perfect fit to explore the latest state-of-the-art in Convolutional Neural Networks for visual feature extraction. An increase in digital art and the digitization of existing artwork combined with the improvement of machine learning enables large-scale art classification models. And transfer learning from real-world image classifiers is enhancing art classification effectively. In our case, the most frequently used pre-trained model would be AlexNet, a model from 2012 which can still achieve state-of-the-art performance when combined with many tricks having proven to increase model performance.
Classifying digital art such as NFTs makes sense since there are more and more available pieces available online, and the traders are showing tight clustering behavior with the object feature they like the most in images. Thus visual features are nonetheless helping NFT sales prediction tasks, but they could also help art purchases match a buyer’s feelings, mood, and personal history, or even provide a self-fulfilling experience.
Similarity search engines trained on NFT classification models will unlock smart recommendations of NFT art based on a buyer’s profile.