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I'm wondering if a given number of images per second, say 15000, means that 15000 images can be processed by iteration or for fully learning the network with that amount of images?. Typically they specify somewhere whether they talk about the forward (a.k.a. inference a.k.a. test) time, e.g. from the page you mentioned in your question: Another example ...


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The ImageNet data is online: http://www.image-net.org/. The data descriptions says: 14,197,122 images, 21841 synsets indexed However, you don't need to train a model on all of the data from scratch, since you can use pre-trained models, e.g. in TF/Keras. See the docs: https://keras.io/applications/. I don't think the original Alexnet is available, but ...


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I think you got most of it from the way you wrote your question. How does R-CNN and AlexNet compare? Are they used for the same purpose or R-CNN does more? They are different things. AlexNet is a CNN architecture, i.e a neural network with a specific set of layers. R-CNN is multistep method that does object localization and classification using a search ...


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Potentially what you could do is use a GMM with 3 modes to cluster the image into 3 "partitions". The first would contain all the pixels with blue, the second all the pixels with brown, and the final should contain the background pixels. You would cluster based on the raw RGB values (unless you could calculate some more semantic deep learning based feature ...


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I think the confusion with the Inception module is the somewhat complicated structure. The point on the relevant CS231n slide (#37), saying there are no FC layers is partially correct. (Remember this is only a summary of the model to get the main points across!). In the actual part of the model being explained on that slide, they are referring only to the ...


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