<ul><li>The article highlights the importance of data quality in machine learning models, emphasizing that most failures stem from data issues rather than architectural flaws.</li><li>The author discusses common data misalignment problems like incorrect annotations, class imbalances, and bounding box errors that often go unnoticed but severely impact model performance.</li><li>The Yololint tool is introduced as a solution to help ML practitioners critically evaluate their datasets before training, by checking directory structures, annotation consistency, class frequencies, bounding box validity, and more.</li><li>Yololint aims to make invisible data quality issues visible to prevent them from compromising the accuracy and reliability of machine learning models.</li></ul>

Your model isn’t lying. It’s just silently learning from data you haven’t fully understood.

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