The article explores the evaluation process of machine learning models, emphasizing its importance for cleaner data and improved performance. It contrasts the evaluation of trained versus deployed models and discusses the significance of metrics like accuracy and F1 score, cautioning that they can be misleading as class numbers increase. The author advocates for a manual review process post-training, focusing on images the model misclassifies or has low confidence in to ensure stringent adherence to labeling standards. This thorough evaluation helps maintain data integrity and prepares models for real-world applications.
The evaluation process is crucial for improving model performance; manual reviews of misclassified images ensure labeling accuracy and adherence to established standards.
Metrics like accuracy and F1 can be misleading as class numbers increase. Manual image reviews are essential to confirm labeling quality and model reliability.
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