WTF COCO - The Weird Images that Underpin Modern Computer Vision Models

COCO is a widely-used benchmark dataset for evaluating object detection models, known for its extensive collection of images depicting everyday scenes with over 1.5 million object instances across 91 categories. Despite its prominence, the dataset contains peculiar and sometimes inaccurately labeled images, which can lead to surprising search results and highlight the importance of understanding the dataset's limitations. Researchers often use COCO as a starting point to train custom models efficiently by building on its pre-trained checkpoints, but they must be cautious of relying solely on its mean average precision (mAP) as a performance metric. The text emphasizes the need for a thorough evaluation of models across multiple datasets, including novel ones, to ensure their effectiveness in real-world applications. It also advocates for a data-centric approach to improving model performance by expanding and refining datasets, rather than focusing solely on model architecture. Ultimately, understanding and addressing the quirks and errors within datasets like COCO is crucial for advancing computer vision and ensuring robust model development.