What are the features of segmentation training on high-resolution images?

Segmentation training on high-resolution images has several features:

• High computational costs: large images require more memory and processing time, which may necessitate the use of powerful hardware or techniques like resizing (e.g., pipelines with crops).

• Scaling issues: details in high resolution can be small, so the model must consider both global context and local features.

• Use of tiling approaches: images are often divided into smaller patches, the model is trained on them, and then results are combined back to preserve detail.

• Data augmentation: various transformations are applied to improve model robustness, considering that small details can be critical.

• Balance between accuracy and speed: sometimes resolution or accuracy is sacrificed for acceptable training and inference times.

• Model architectures: specialized networks like U-Net with attention mechanisms are often used, which work well with details at different scales.

Example of a tiling approach:

1. Divide a large image into overlapping patches.
2. Train the model on these patches.
3. During inference, combine patch predictions into the final segmentation mask.