Cleanlab for E-Commerce and Retail

If the classifier is trained with these noisy images directly, its performance could be degraded. In view of this, we attempted to find label errors in the image dataset with an open source tool cleanlab, a framework powered by the theory of confident learning. Specifically, we trained multiple ResNet50 image classifiers to compute the predicted product category probabilities for all the training samples in a cross-validation manner. Then the cleanlab tool could utilize the matrix of predicted probabilities to find noisy samples, ordered by likelihood of being an error. We removed the top 10% noisy samples from the training set.