recent advances in deep learning maintenance and computational cost and improve coverage by removing the need for separate model for each vertical application groknet leverages multi-task learning approach to train single computer vision trunk the model was trained over distinct datasets across several commerce verticalsusing categorical loss functions and embedding losses the final model predicts the following for given imageobject category"bar stool,"scarf,"area rug,etc home attributesobject colormaterialdecor styleetc fashion attributesstylecolormaterialsleeve lengthetc vehicle attributesmakemodelexternal colordecadeetc search queriestext phrases likely used by users to find the product on marketplace search image embeddinga -bit hash used to recognize exact productsfind and rank similar productsand improve search quality with such rich prediction for given imagea marketplace feed for given user' search results can be tailored and customized with highly relevant results the image embedding predicted can be further used to present similar product listings so that user can make more informed decision moreoverthis entire augmentation task is performed by single model rather than collection of models for more information about groknetvisit research/publications/groknet-unified-computer-vision-modeltrunk-and-embeddings-for-commerce