I am looking into making my custom YoloV5 model faster, based on my current results where I have trained a on ~20k (1280 × 960) images with a configuration based on yolov5l6.yaml (A P6 model I assume)
This gives me a very well performing model, and I would now like to explore smaller/simpler models.
Option A: Is the simple and try to train with a medium/small/nano version of the P6 model
Option B: I would like to try going to an image size of 640 by using a P5 model.
My question is: do I need to manually resize my images to 640x640 to efficiently train a P5 model (e.g. based on yolov5m.yaml), or can I simply pass the desired image size as a parameter to train.py?
The imgsz flag determines the size of the images fed to the model, this is also true for training. So you can simply do the following
python train.py ... --imgsz 640 # training on 640 images
To check that this is actually the case you can
print(img.shape)
just before the image batch is fed to the model