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The paper A Brief Introduction to Statistical Shape Analysis (2002) by M. B. Stegmann and D. D. Gomez provides a definition of a landmark in the context of statistical shape analysis, which I will report below. Definition 1: Shape is all the geometrical information that remains when location, scale and rotational effects are filtered out from an object. ...


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Image to Image translation is the task of transferring an image's characteristics from one domain and representing it in another. GANs have provided an end to end method to do this task. Prior to Gans, these tasks were done individually, by using classic image processing techniques mainly. Techniques such as image denoising, or finding edges in photos, or ...


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try using an adjustable learning rate. Keras has a number of callbacks that are useful for this purpose. The ReduceLROnPlateau callback can be used to monitor validation loss and reduce the learning rate by a factor if the validation loss does not decrease after a user specified number of epochs. The ModelCheckpoint callback is useful to monitor the ...


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There is no easy rule for this. You can use transfer learning to select a model that works well on image classification. However the accuracy you achieve will be highly dependent on your training set. If your training set is "similar" in quantity and quality to what was used for the accuracy achieved by the transfer learning model in some application you ...


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If using TFLite python API, you may reach to 1/2 FPS. On downscaled custom Pose architectures you may get more FPS. You can try the TFLite c++ API as well, it might be a bit faster than the python one. To reach more than this you would need a TPU or a library that could use GPU computation of the Pi. Nevertheless, I doubt that it would reach 15 FPS with ...


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ResNet is an architecture for object recognition and you may use it to do your classification task. Fast RCNN may improve your results but is a more difficult architecture to implement. If you want to go in this direction the best place to start is the arxiv paper of the Fast R-CNN (arxiv.org/abs/1504.08083). If I am not wrong, there is an implementation ...


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The main distinction between tasks is 'classification' vs 'regression'. In classification you would try to identify the presence of a cloud or not in an image, if you want to predict the level of 'cloudness' with continuous values you are then performing a regression task. I'm not aware about state-of-the models specific for images, but you can potentially ...


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