New answers tagged cnn
1
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 $= 501\times 128 =64128$
3
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.
1
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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