# Using Contrastive Learning and Pseudolabels to learn representations for Retail Product Image Classification Source: [Using Contrastive Learning and Pseudolabels to learn representations for Retail Product Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:37:16.577Z Authors: Muktabh Mayank Srivastava Published: 2021-10-07T17:29:05.000Z ## Abstract Retail product Image classification problems are often few shot classification problems, given retail product classes cannot have the type of variations across images like a cat or dog or tree could have. Previous works have shown different methods to finetune Convolutional Neural Networks to achieve better classification accuracy on such datasets. In this work, we try to address the problem statement : Can we pretrain a Convolutional Neural Network backbone which yields good enough representations for retail product images, so that training a simple logistic regression on these representations gives us good classifiers ? We use contrastive learning and pseudolabel based noisy student training to learn representations that get accuracy in order of finetuning the entire Convnet backbone for retail product image classification. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()