How to Manage Classes in Object Detection (Rename, Combine, Balance)

In computer vision, renaming classes, combining classes, and handling class imbalance can be tricky and time-consuming. We walkthroughs strategies to more easily improve the performance of your object detection models through class management. We also demonstrate the performance difference an example YOLO model has when trained on balanced vs unbalanced classes as an example.
In this video, we make use of Roboflow Pro's Modify Classes features. Get access to Roboflow Pro here: roboflow.com/sales
We've written about class management here as well: blog.roboflow.com/introducing...
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[@faridbeheshtinia742]

first of all, thank you very much Joseph for your incredible tutorials. As a beginner, I've learned a lot of cool things with your help.
I wondering if you could upload a tutorial for license plate recognition.
for license plate recognition it's essential to detect the plate and after that recognize the alphabet and numbers and put them on the box over the plate! I'm not sure about it pls tell me if I'm wrong!:)

[@shahriaralom4547]

Thank you so much but class rename is not working, I have tried 3-4 time but it shows the same names

[@priyankthakkar7849]

How to see the health check of the new version of the dataset generated?

[@houston3414]

Thanks for the tutorial I got a lot from roboflow, can you do a tutorial about metrics like precision & recall and compare them on model used by robolflow vs without roboflow

[@Roboflow]

Certainly! We've published about mAP and other metrics here, as well: ruclips.net/video/oqXDdxF_Wuw/видео.html

[@akankshaprasad6857]

Can you please tell me how to annotate large number of images

[@alhasanalshaebi]

Can u share your Colab experiment?

[@wiwatpitakworarat6816]

Relabel is not work at all. Relabel from 0 to car and then after export data we get labels is still 0.