Learn how expert-level labeling workflows build smarter and more accurate AI vision systems.
Use data augmentation to expand and diversify the training dataset.
Standardize images through resizing, normalization, and noise reduction.
Apply consistent bounding boxes, even partially visible objects.
Define precise labeling criteria and naming conventions.
Prevent overfitting by ensuring each split serves a clear purpose.
Divide data into training, validation, and testing sets.
Use inter-annotator agreement metrics to ensure labeling consistency.
Maintain high precision, recall, and IoU accuracy.
Expand datasets and retrain models for improved performance.
Analyze model feedback to identify labeling gaps.
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