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I think you might have misunderstood 2 concepts here: CNNs and Object Detection. Object Detection is an AI approach to solve problems where you are interested in both the location and the classification of key elements in the image. On the other hand Image Classification is another approach where you are interested in classify the whole image with a tag. ...


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The computer vision problem that you are describing is object detection, i.e. the problem of finding the location of specific objects in an image and label them correctly with their names. There are many resources on the web (or in books) that describe this problem more in detail and examples (which also include code) to get you started with it (e.g. this ...


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The ideal hyperparameters is usually dependent on your dataset and will differ on a case by case basis. Go for trial and error to determine the hyperparameters that works best for you. Few research papers similar to your use case is listed below. CNN transfer learning for visual guitar chord classification A Study of Left Fingering Detection Using CNN for ...


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Yes, theoretically it is possible to learn the offsets to get any possible bounding box from only one anchor box. However, it is hard to learn such dramatic shifts and changes. Learning only small offsets from the prior is easier and tends to converge better. In specific applications however, one might already know the typical size and ratio of objects, and ...


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