Advice on how to proceed with fine tuning Yolo26n.pt locally with Python?
I haven't trained or fine tuned a model yet. But I have been using the model to detect wildlife and have been saving the images. I use the model to help me run moose off from the garden, but the dataset doesn't have moose in it, so I have it detect horse and cow and assume it's a moose and it works well. But now I'd like to make something a bit more accurate using my linux machine with an NVIDIA Pro 6000 Blackwell with 96GB of VRAM and Python. I have a few questions.
a) I have 415 images my cameras have taken of moose, and the images include the bounding box (no label) around the moose, will this work?
b) I have 54 images of bears taken from my cameras, would it help to add them to the dataset even though the COCO dataset already includes bears, given it would factor in my landscape or would it make things worse?
c) I have about 3500 images of the wife and myself, could I include us as a category so the model can distinguish between us and others or would that just confuse things?
d) My images of moose only puts one bounding box around one moose even when there are more in an image, should I ensure bounding boxes around all of them before using them for fine tuning?
e) I assume I would make a .yaml which would point to a folder structure of my new images and their labels before running the python command to fine tune, should I include the entire COCO dataset to prevent memory loss of the originals?
f) I can only use 18 of the objects in the COCO dataset, would it make sense to download the COCO training dataset and delete "fire hydrant" and "bus" and all the things that we will never see out here in the forest?
Many thanks for any pointers or advice, I know it's a noob question and shows that I have barely done any research.