New answers tagged convolutional-neural-networks
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How can I use larger input images when using a pre-trained CNN without resizing?
TL;DR:
It's definitely worth trying to benefit from the learned features from the ResNet. As it's made of mainly pretrained convolutional layers with pooling, adding new resizing layers upfront is ...
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Detecting object position given the relative position of another object
I always like to think that Theoretically, if there exists some function $f:R \rightarrow B$ that maps the set of points $R$ which represent your reference object to the set of points $B$ which ...
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Is this aggregation of multiple convolutions of the same input a type of attention or dynamic convolution?
See Dynamic Convolution: Attention over Convolution Kernels by Yinpeng Chen et al.
The convolution kernels are generated by taking a weighted average of K=4 kernels....
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What's the difference between architectures and backbones?
I've taken an NVIDIA course on the portal and it said that ResNet, VGG, GoogleNet were model architectures , and that DetectNet_V2,FasterRCNN,SSD, UNET were model backbones, so I think it's a common ...
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What data can I obtain from CNN model (H5 file)?
We can't see the BatchNorm layer in Netron, so NO. It doesn't have BatchNorm but for good reason. In testing, you don't need BatchNorm (that is the possible reason why it has been done).
Second, for ...
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Is data augmentation beneficial even if the dataset is large/diverse enough?
You are going to generate the images by flipping, rotating, etc. which will happen anyways in augmentation. Augmentation can happen on the fly so you don't waste memory storing those new images, thus, ...
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How can I implement 2D CNN filter with channelwise-bound kernel weights?
In PyTorch implementation of convolution modules, you can just set the out_channels and groups argument to be the number of your ...
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During batch normalization is the mini-batch gone through twice, one to calculate the mean and variance and then to normalize them?
I think it'd be helpful to refer to the batchnorm formula given in the PyTorch implementation.
In particular, given an input $x$, you would get the mean and variance ($\mathbf{E}[x]$ and $\text{Var}[x]...
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How is a filter actually applied to all input channels in a ConvLayer2D
If you have a conv layer that has 3 input channels and 32 output channels (i.e. the number of filters), then you essentially have $3 \times 32$ convolution operations connecting every input channel to ...
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