So I've got a neural net model (ResNet-18) and made a diagram according to the literature (https://arxiv.org/abs/1512.03385).

I think I understand most of the format of the convolutional layers: filter dims ,conv, unknown number ,stride(if applicable)

What does the number after 'conv' in the convolutional layers indicate? is it the number of neurons in the layer?

ResNet-18 architecture

bonus q: this is being used for unsupervised learning of images, i.e the embedding output a network produces for an image is used for clustering. Would this make it incorrect for my architecture to have an FC layer at the end (which would be used for classifcation)?


1 Answer 1


This number refers to the number of kernels (or feature maps) that are convolved with the input. So, for example, in the first convolutional layer, $64$ $3 \times 3$ kernels are convolved with the image.

The ResNet presented in Deep Residual Learning for Image Recognition is used for image classification. Furthermore, note that your diagram already contains a fully connected layer at the end.

  • $\begingroup$ Are those all distinct kernels or is that the total number of convolutions that a particular 3x3 filter must perform when passed over all the pixels in an image? I added the FC layer at the end because I copied the format of their model, however mine isn't used for classification so should I remove the FC layer and possibly the average pooling? $\endgroup$ Commented Aug 7, 2019 at 10:24
  • $\begingroup$ @thatsnotmyname71 The kernels in a CNN are learned, so they will likely all be distinct. What do you mean by the embedding output a network produces for an image? Maybe you could ask a separate question (in a separate post) with more details regarding your specific problem. $\endgroup$
    – nbro
    Commented Aug 7, 2019 at 10:54
  • $\begingroup$ ahh, thank you, I understand the kernels now. The network is used for unsupervised learning, so an output embedding for image triplets (which allows the use of a loss function encouraging embeddings which separate the 'anchor' image from its 'distant' image. The concept comes from Tile2Vec/Word2Vec (arxiv.org/abs/1805.02855). $\endgroup$ Commented Aug 8, 2019 at 8:29
  • $\begingroup$ @thatsnotmyname71 I will have to read your linked paper before trying to give you an appropriate answer. $\endgroup$
    – nbro
    Commented Aug 9, 2019 at 11:04

You must log in to answer this question.

Not the answer you're looking for? Browse other questions tagged .