Whisking Away Errors: How Cleanlab Studio Served Up Fixes for the Food-101N Computer Vision Dataset
Blog post from Cleanlab
The Cleanlab Studio Audit (CSA) is a tool that uses AI to identify issues in popular datasets, such as the Food-101N dataset, which contains 101k images with 101 food categories. The audit revealed thousands of label issues, outliers, ambiguous examples, and near-duplicates in this famous computer vision dataset. Cleanlab Studio automatically identified mislabeled images and suggested more appropriate labels, including a piece of cheesecake labeled as a carrot cake. The authors of the Food-101N dataset noted that it had more images and was noisier than the original Food-101 dataset, but did not mention these issues in their disclaimer. Cleanlab Studio found 27,488 mislabeled examples, 8,519 outliers, 13,538 ambiguous examples, and 17,510 near-duplicate examples, which are detrimental to modeling and analytics efforts. The tool helps data owners identify and fix such errors to train the best models and draw accurate conclusions.
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