Abstract: For classifying products, a method trains a supervised learning product model, wherein the product model embeds product embeddings of a same product close to another product in a latent space of a vector database. The method further generates the vector database of the product embeddings for the plurality of the product images, wherein the vector database comprises product embeddings of known products and unknown products. The method generates a new product embedding for a new product. The method queries the vector database with the new product embedding as a centroid for a proximity query, wherein the new product embedding is a novel distance from other product embeddings in the vector database. The method labels close product embeddings from the vector database as the new product. The method adds the new product to the product detector using product images extracted from within a group of the vector database.