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If you want to know whether a CNN would accept pixel values of [0, 1] or [0, 255], you need to look at its documentation or model architecture. E.g. It's explicitly written down that EfficientNet accepts [0-255] here. And if you look at the architecture, you will see that it comes with a normalization layer to convert [0-255] into [0-1]. Sometimes the CNN ...


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If you need a tutorial on how to code the classification algorithm check out this article.


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$(5\times 100)+1$ no of parameters for the single filter, $+1$ is for bias term for each filter and we have 128 such filters so total no of parameters $= 501\times 128 =64128$


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The authors provide this image in their supplemental information: There, you can see their explanation. The convolutional layers encode the image into some latent space representation. The RNN operates in this latent space, generating a new latent space representation based on the previous observations. For any latent space representation, the decoder can ...


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"(V)" only appears in a single Conv block, that term otherwise only appears in AveragePool. In the text as the following description: An average pooling layer with 5×5 filter size and stride 3. Which implies that "(V)" is also a stride. Implementations encode the term as a stride.


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If all of the carts images are similar (and different from the images without carts), the classification problem is easy. Therefore, it is not a problem, but an advantage. That is under the assumption that the images' distributaion in inference is the same distribution as in your training.


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I think what you are looking for is numpy.concatenate. From the documentation this allows you to: Join a sequence of arrays along an existing axis. It has the function signature: numpy.concatenate((a1, a2, ...), axis=0, out=None, dtype=None, casting="same_kind") See the documentation for more.


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