How to Identify Mislabeled Images in Computer Vision Datasets

Ensuring data quality is crucial for developing effective computer vision models, and this guide outlines how to identify potentially mislabeled images within datasets using CLIP and the Roboflow CVevals project. By uploading annotated images to the Roboflow platform, users can utilize automated checks to enhance data quality and manually inspect annotations. The guide details the process of using the cutout.py script from the CVevals project, which calculates CLIP vectors to spot discrepancies between annotations and average class vectors, indicating possible mislabeling. After downloading and preparing the necessary script and dependencies, users run the script using specific arguments to evaluate images in their dataset, generating a report that highlights potential labeling errors. The guide emphasizes the importance of this evaluation in maintaining dataset integrity, thus improving model performance, and suggests that users run such analyses before training new model versions to mitigate the impact of incorrect annotations.