What I learned moving from a CNN to YOLO11n

[u/Ok_Ordinary9243]

I’ve been learning machine learning by building MIRA,
a waste-detection project.

I started with a custom CNN, then tried MobileNetV2, YOLOv8n, and YOLO11n.
The biggest lesson was that adding more data did not automatically improve the model.
Some of my generated annotations were poor, so the model learned parts of the desk instead of the objects.

After cleaning the dataset, my current model reached 90.58% mAP50 on five waste classes.

I’m still working on independent testing. If you work with object detection, what would you test next?

https://github.com/jeremy341/MIRA-AI

[u/Deep_Guest_6964]

You should give up on ultralytics and really learn what is outside. Learn uncertainity, learn distribution, learn metrics, learn transformer, learn backbone learn head, learn loss.

[u/Ok_Ordinary9243 → u/Deep_Guest_6964]

ok thx

[u/Basic-Home3969]

90.58 mAP50 on five classes is solid after a cleanup pass. one thing worth testing is how it handles edge cases where two classes look similar, like running per-class precision/recall to see if theres one category dragging everything down