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....

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

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

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.

Sounds like you need to label the chairs.

If you already have some labeled chairs, train a first model with that, then run it on images with chairs and no label. Have a second pass with your enriched dataset, and eventually a third, etc.

You can bootstrap the labelling that way. It should help you label a decent amount of chairs, and you can then label manually the remaining chairs.

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.

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?

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?

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

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It might not affect training as much as you think - you could also modify your loss function so it doesn’t penalise false positives as much as false negatives

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

I think you can do it both ways (with or without filtering) and compare. Just speculating now, but the filtering could potentially affect the performance on the other classes (since you change the model’s training path for those classes ). But my guess is that that should not be a big issue, so I’d probably go about it the way you described if I had to pick one strategy.

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

Good luck! Would be great to hear how it goes and which insights you get!

Try the easy way first :

Build a model that only learns chairs, with all labeled chairs you have and ignore anything else at first.

Try also image data augmentations and see if it helps.

You are not looking at having the best score, actually you dont care about your score as long as you can label new chairs.

You mostly want to tune the model so that you don't have false positives (and introduce noise in your labels). False negatives are OK, and will occur if you tune the model so that FP are zero. You can tune for instance the threshold on a confidence score or class probability (check the model you're using).

You can also build a basic image validation tool with jupyter notebook widgets, steamlit, or your favorite tool, if you want to validate quickly by hand that they are no false positives. It's a very good exercise.

Good luck !

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!