What are the best practices for image segmentation in AI?

What are the best practices for image segmentation in AI?

Some best practices include using high-quality annotated datasets, choosing the right model architecture, and applying data augmentation.

Can you explain why data augmentation is important?

Data augmentation helps improve model robustness by artificially increasing the diversity of the training dataset, reducing overfitting.

What model architectures are commonly used for image segmentation?

Common architectures include U-Net, Mask R-CNN, and DeepLab, each with unique strengths for different tasks.

How do I evaluate the performance of my segmentation model?

You can evaluate performance using metrics like Intersection over Union (IoU), pixel accuracy, and F1 score.