You've got a trained YOLOv8-seg model, but you're feeding your entire image into a second classifier. Stop doing that. You're poisoning your classifier with useless noise from the background. You need to isolate the muzzle, and there are only two ways to think about it.
This is the fastest method. YOLO gives you a bounding box—a simple rectangle. You use its coordinates to crop the original image. It’s quick, but it's sloppy. You're still including background pixels that aren't part of the muzzle. It's better than nothing, but we can do better.
This is the method you should be using. Your YOLOv8-seg model provides a precise pixel mask for the muzzle. You use this mask to create a new image where every single pixel that is not the muzzle is blacked out.
The result? An image containing only the muzzle pixels. Zero background noise. You are feeding your classifier exactly what it needs to see and nothing more.
The verdict is simple: If you're serious about accuracy, use the mask. Cropping with a bounding box is a shortcut that leaves performance on the table. Isolate your object properly and stop feeding noise to your models.