Performance drop due to multiple classes

[as51340]

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Question

Is it expected that the detector works better when fine-tuned only on one class then when trained in environment with multiple classes? E.g. the detector is much better on class Person when fine-tuned with a dataset containing only Persons than when fine-tuned on dataset containing Person and Sports ball.

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[glenn-jocher]

@as51340 hey there! When you're fine-tuning on a dataset containing multiple classes, the model's performance can be influenced by the complexity and diversity of the classes. This can sometimes lead to performance variations compared to training on a single class. It's not unusual to observe different results when training with diverse classes, as the model needs to learn to distinguish between a wider range of objects. Fine-tuning on a single class may lead to higher accuracy on that class due to the focused training. If you need further guidance, you can refer to the training tips in our Ultralytics Docs. Keep up the good work!