training a custom object detection model from scratch (yolov8) (intermediate) anthony explains #571

today I walk through my adventures training a custom object detection model (for identifying pokemon)!

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I won't ask for subscriptions / likes / comments in videos but it really helps the channel. If you have any suggestions or things you'd like to see please comment below!

[solevoi_kreiser]

As a data scientist, I can surely tell you that the image size and batch size arguments are most of the time to play around with. There are even libraries such as Optuna which find the best combination in terms of a chosen metric using cross-validation and Bayesian optimization, for any framework, and for any hyperparameter combination, but it would take respectively 10x time. Usually batch sizes are in the 2^n range. Also to avoid false positives, you can set minimum confidence value to set label to the minimum conf value of TP label, which here is around 0.8. As I said, all these steps are very dependent on the dataset and are almost always different.

[JakubYTb]

Somhow I knew it will be to detect pokemon :D
Detecting pixel color will take you just so far. Great work and a nice video :-)

[Andrewlstewart]

As a data scientist who follows this channel to become a better dev, this was wonderful. "Because someone told me to" ❤️

[senseikoudai]

What was the intended use case?
Is it possible to use the trained model in Python code and you wanted it to run live while playing?
Wouldn't this just help you "find" one on video, therefore after playing when you can't catch it anymore?

Or was this all just a way to learn something new and you just wanted to see if you could find something in an image?

[DionBoles]

This was a great tutorial I wanted to know how to train a yolo model. Could you do the tutorial for finding pink on the screen?

[redsolaris]

Those file image sizes could be an issue