How to take advantage of additional data?

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I am trying to train a 3 class problem object detection problem,For these 3 classes i have around 9000 samples each.The model is performing decently but there is still confusion between classes.

I have additional data for these classes but this data is NOT annotated, i only have the images, i could probably annotate them for what class they belong to, but annotating bounding boxes will not be possible.

is there something i can do to take advantage of this data that i have?may be some pretraining or something, im not really sure.

Any suggestions will be helpful.Thanks in advance.

Maybe you could try to do something similar to pseudo labeling: Training the model on the annotated images and then predict the additional data and use the predictions as labels and retrain the model on the old data + the newly “annotated” data

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Yes, I’m trying that, but my concern is that because ill only use good predictions by the model, i wonder if the model will learn anything new!

You can do active learning to select which new data to label.

To use the data directly, the following paper describes some techniques:

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