I see. I think changing the threshold of confidence score and probaility is good idea. I should try the ways step by step. Thank you!

Ok I appreciate! I'm trying to filter out images.

Ok I will try to modify loss function as you say. Thank you!

I got it. Thank you so much for your answer.

Thank you. But I don't understand it clearly.

Do people train the model in that way as well? In the COCO dataset, some images contain objects that are not labeled but are listed in the classes.

If people follow your suggested method for training the model, they would need to first filter out images with perfectly labeled objects (no missed labels) from the COCO dataset and use that filtered data to train the model. Then they would need to run the model on the remaining data to obtain labels for objects that are not included in the dataset, and update the entire dataset accordingly. Is this correct?

So do I need to label chairs in all the images?

Ok, for example, there is famous dataset which is COCO dataset. But some objects, for example 'book', exist but are not labeled in some images (not all. many of images have labeled 'book' object). And people use the dataset for training and detect 'book' object well somehow. I just want to know how they handle the unlabeled 'book' in some data.

I need the chair class for the model, but some images don't have the label of chair even though the images include chairs. And I want to use those images for training because they include other classes that should be trained.

Thank you for the comment. So do I need to modify images to remove chairs one by one by manually?