How to training yolov8 include slice aided hyper inference (sahi) for deploying model to android

[rahmadyd]

excuse me, i want to ask about How to training yolov8 include slice aided hyper inference (sahi) because i want to export the weight but i dont know where i put sahi code if i export that weight to tensorflowlite (i want to deploy this model to android). please tell me how it works. thanks

[glenn-jocher]

@rahmadyd to train YOLOv8 with SAHI for sliced inference and then export to TensorFlow Lite for Android deployment, you first train your model normally with YOLOv8. SAHI is used during inference, not training, so you don't need to modify your training process. After training, you can export your model to TensorFlow Lite using the export function. For integrating SAHI during inference on Android, you would handle the slicing and stitching logic in your Android application code. For detailed steps, please refer to our documentation: https://docs.ultralytics.com/guides/sahi-tiled-inference/.

[agoransson]

I've been looking for that answer for very long - I've previously understood that SAHI requires special training logic, to slice training images in the same way as images are sliced during inference. You're saying this isn't needed, "any" Yolo object-detection model should do fine while running inference with SAHI?

Thank you.

[glenn-jocher]

@agoransson that's correct—SAHI is used only during inference to handle large images by slicing them into smaller tiles. Any YOLO model trained on standard datasets should work fine with SAHI for inference without requiring special training logic.