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I would classify each pixel separately instead of giving a label to the whole image. Sadly preparing the training data is very tedious and time-consuming. Let's say the input image has dimensions of 200 x 300 x 3 (RGB) and there are two classes of regions you want to identify. A few approaches come to mind: 1) Train two separate networks, each forecasting ...


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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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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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Couldn't find the specific India dataset for traffic signs, but here is the generalized one from the Open Image Dataset by Google link Maybe, you can classify them based on the Indian standards, or there may be an option to request images that were tagged in India (location)


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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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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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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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