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I believe that the idea is to have a similar ratio of fraud/"normal transaction" as to the ones that bank encounter on real life. If you balance it you will probably have a lot of false positive once you apply your solution to real world's data and, if that may be fine for you to play with, it's not what a bank would like as they can't block too much of the ...


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Look at Google's Open Image Dataset @ https://storage.googleapis.com/openimages/web/index.html They provide image-level labels, object bounding boxes, object segmentation masks, and visual relationships. Here is the link for the traffic signs dataset.


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You can't label things you don't know. The goal of labeling is to label the things you want the classifier to learn so that when you run it in inference mode you can discover what is in your data (new data that you didn't use for training, validating, or testing). It is not a good idea to label small objects like the 'blue water' unless it is important to ...


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I think what you are actually talking about is semantic segmentation (where you label pixels individually). There is a difference in theses tasks like Classification, Detection or Semantic Segmentation. Classification refers to the task of giving a (usually) single label to the whole image, e.g. cat. But as you already noticed this does not nececerraly ...


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Answering my own question here. Looked at the Open Image Dataset by Google @ https://storage.googleapis.com/openimages/web/index.html They provide image-level labels, object bounding boxes, object segmentation masks, and visual relationships.


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