How to Use Tiling During Inference

Tiling is a technique used in computer vision to enhance the accuracy of detecting small objects, particularly in aerial images, by dividing an image into smaller, more manageable sections for both training and inference. This approach allows models to focus on smaller details, similar to zooming in, which can improve detection accuracy significantly. However, it is crucial to apply tiling consistently across both training and inference processes; otherwise, it may lead to false positives, as seen in models trained with tiling but inferred without it. While tiling boosts accuracy, it can negatively impact performance by increasing training times and slowing down inference speed due to the generation of numerous smaller images. Therefore, finding a balance between accuracy and performance is essential, and experimenting with different tile sizes can help achieve optimal results. Despite these challenges, tiling remains a valuable method for enhancing object detection in specific scenarios, though it is no longer supported as a parameter in the Roboflow Hosted Inference API, requiring alternatives like Slicing Aided Hyper Inference (SAHI) for implementation.