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I am afraid that the model will infer from the background information that it shouldn't use to predict the plant diseases, what makes the problem worse is that some plant diseases only exist in one dataset and not the other I am afraid that when I use the model on real-life conditions (say someone capturing an image with their phone) the model will be ...


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In image segmentation the target is actually an image, with the same dimensions as the input, where each pixel has a label depending on which class it represents. It is not uncommon for such a dataset to have a "background" class that essentially consists of the pixels not belonging to any other class. If not you can always group together classes ...


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Check out confident learning: https://arxiv.org/abs/1911.00068 Essentially, you train a model, then relabel the instances with low confidence, then retrain, etc...


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