How Do I Train a Model for Defects I Almost Never See?

In the realm of defect detection, particularly for rare defects in manufacturing, the challenge lies in addressing the significant class imbalance typically present in datasets, where most examples are defect-free. To overcome this, strategies such as using high-resolution images, careful annotation, and class-aware loss functions are essential. Techniques like tiling and augmentations enhance the visibility of tiny defects, while synthetic data generation and transfer learning utilizing pre-trained models can improve model performance with limited data. Active learning plays a crucial role by identifying and incorporating new rare defects encountered during deployment, thus continuously improving the model. Evaluation should focus on specific metrics for rare defects rather than overall accuracy. The Roboflow platform supports these processes by facilitating project creation, data management, model training, evaluation, and deployment, enabling an iterative cycle of improvement suited for environments where defects are seldom seen but critical to detect.

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