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mmeeh t1_jdupe4m wrote

Either you label the chairs in the non-labelled images or you remove the chairs from the labelled images... You can try some thechniques of image manipulation to remove the non labelled chairs but unless you use photoshop or some high advanced AI to remove those chairs, it's a really bad idea to use that dataset to train a model to recognize objects that include chairs....

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AI-without-data OP t1_jdurfc6 wrote

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

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mmeeh t1_jdusxmd wrote

each of this BBOX has a label when you are training with yolov, you can just write some code that identifies the BBOX which have the label of chair so you can exclude them from the dataset

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AI-without-data OP t1_jdv5l7k wrote

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.

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thebruce87m t1_jdvby1c wrote

Sounds like you need to label the chairs.

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AI-without-data OP t1_jdvfwff wrote

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.

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thebruce87m t1_jdvhv7r wrote

If you want it to be good at detecting books then yes, all books should be labelled.

If they are not, what are the implications? Perhaps book detections will have lower confidence than they should? Maybe it will ignore some styles of books?

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