Hi everyone!
I’m working on an object detection project for a smart farm surveillance system. I’m using YOLOv8n as my base model, and I would like to add two new custom classes (“fire” and “smoke”) to it.
My goal is to:
- Keep all the 80 original COCO classes
- Add my own classes on top: class 80 = fire, class 81 = smoke
- Avoid retraining from scratch (just fine-tune with my own dataset for the new classes)
Any guidance, tips, or success stories from people who’ve done something similar would be hugely appreciated!
Thanks in advance!
— Anis Ghabarou
(Working on a Raspberry Pi 4 model b-based surveillance solution)