I've recently trained a custom yolov5 model to recognize animals on safari.
Animals on safari are far away most of the time, and so, after resizing images to 640x640, most of the animals are now too small to be detected.
I've researched the technique of tiling, taking a large image and splitting it into 5x5 smaller images, allowing the inference not to take up as much ram as trying to run the inference on the initial large image. However, there is no instruction on how to do this on real-time inference.
The model I'm using is Yolov5 trained with PyTorch.
Does anyone know how to do tiling on real-time inference?
