So I’m training my custom model to identify my cats on my outdoor CCTV cameras. After reading the yolov5 training tips, I believe running img size 1280 would be beneficial for my use case. The cameras are 4k and mounted quite high to cover more area, this results in the cats that i want to detect are a very small part of the total picture.
As the FAQ mentions, it’s recommended to run the detection at the same image size as the training, my question is therefore if there’s any way to run deepstack detection with the same higher image size? (currently running windows 10 native GPU).
Feeding Deepstack larger images increases processing time, is this because the actual size is processed fully (in that case i can adjust the input size), or are all pictures resized to 640px anyway when processed, and the processing time increase is basically just an increase in time the resizing operation to process the larger input image?
I’m sorry if the question is a bit all over the place.
- Will deepstack process the input image at its sent resolution or will it resize it internally before running the detection model?
- If it is resized internally, is there a way to set the parameter
-img 1280in the yolov5 detection model?