Questions tagged [convolutional-neural-networks]

For questions about convolutional neural networks, also known as CNN or ConvNet.

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Different predictions across DL Frameworks [closed]

Can anyone give me a reason as to why I can train a neural network in say Tensorflow Flow, build equivalent models in pytorch and keras and any other DL framework, load the weights from the tensorflow ...
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Is there a common way to build a neural network that seeks to extract spatial and temporal information simultaneously?

Is there a common way to build a neural network that seeks to extract spatial and temporal information simultaneously? Is there an agreed up protocol on how to extract this information? What ...
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How to have closer validation loss and training loss in training a CNN

I am using an AlexNet architecture as my Convolutional Neural Network. A learning rate of 0.00007 and 128 batch_size. I have 20000 data and 10% test, 40% validation, and 50% for training. I used 100 ...
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When should we use separable convolution?

I was reading the "Deep Learning with Python" by François Chollet. He mentioned separable convolution as following This is equivalent to separating the learning of spatial features and the ...
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21 views

Computer vision - Can you put more weight on a specific part of the object?

Let's say I'm looking for any item that has a certain shape (outline) in a photo. but I can further classify it only according to particular features, that most of them are expected to be shown only ...
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1answer
47 views

Is it possible to know the distance objects are from camera based on only knowing one object's height?

I am doing a project where I have to know distance a particular object is from camera. In the photo I only know one of the object's height, but I don't know how far away that object is and I don't ...
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2answers
122 views

What is the need for so many filters in a CNN?

Consider the following coding line related to CNNS Conv2D(64, (3,3), strides=(2, 2), padding='same') It is a convolution layer with filter size $3 \times 3$ and ...
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1answer
43 views

How can I train a CNN to detect when a person is smoking outside of shop given images from a video camera?

My friend is working at a pizza shop. He takes cigarette breaks in an area that is covered by the public webcam of our town. I now want to train a convolutional neural network to be able to detect ...
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Cocatenate feature extractor layers with different channels

I have a network architecture for feature extraction and I wanted to concatenate layers of the same dimensions but with different feature channels. ...
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19 views

Choice of loss function for semantic segmentation

I am training a U-Net for semantic segmentation of large medical images (4096x4096px). The two classes are "too" unbalanced. The white pixels are just about 0.1% (or less) of the whole image....
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Pytorch and keras ddqn seem identical, only keras learns

I followed a tutorial for ddqn to beat pong, it beats it with a perfect score in keras, but trying to translate it to pytorch it doesn't learn at all. What am I missing? I pasted all the code for each ...
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CNN validation decreased and then fluctuated forever

I'm working on learning CNN. The problem I encountered was to reduce the amount of validation loss to a number of epochs and then a lot of fluctuation around the error of 0.1. Then the error and ...
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Image Classification for watermarks with poor results

Just starting learning things about tensorflow and NN. As an exercise I decided to create a dataset of images, watermarked and not, in order to binary classify these. First of all, the dataset ( you ...
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Training a CNN for semantic segmentation of large 4600x4600px images

I am trying to implement a CNN (U-Net) for semantic segmentation of similar large grayscale ~4600x4600px medical images. The area I want to segment is the empty space (gap) between a round object in ...
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Model output segmentation maps which are not full

I created a VGG based U-Net in order to perform image segmentation task on yeast cells images obtained by a microscope. There are a couple of problems with the data: There is inhomogeneity in the ...
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1answer
39 views

Comparing a large/general CNN to a smaller more specialized one?

I am still somewhat a novice in the ML world, but I had a strange idea about CNNs and wanted to ask if this would be a valid way to check the robustness of a general CNN that classifies certain images....
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28 views

How to implement or avoid masking for transformer?

When it comes to using Transformers for image captioning is there any reason to use masking? I currently have a resnet101 encoder and am trying to use the features as the input for a transformer model ...
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1answer
56 views

Can we apply transfer learning between any two different CNN architectures?

There are many types of CNN architectures: LeNet, AlexNet, VGG, GoogLeNet, ResNet, etc. Can we apply transfer learning between any two different CNN architectures? For instance, can we apply transfer ...
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1answer
38 views

Pytorch Deep q network not learning and step not stepping towards target

I am trying to create a simple deep q network for rl with conv2d layers. I can’t figure out what I am doing wrong, and the only thing I can see that doesn’t seem right is when I get the model ...
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1answer
37 views

Should I remove the text overlaying some images in the dataset before training the CNN?

If I am attempting to train a CNN on some image data to perform image classification, but some of the images have pieces of text overlaying them (for the purpose of description to humans), then is it ...
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Duplicating calculations in CNN-LSTM architecture

I want to use frames from video game and analyze them using CNN and LSTM. But when I have the model defined like that ...
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1answer
36 views

Research paths/areas for improving the performance of CNNs when faced with limited data

I've been reading through the research literature for image processing, computer vision, and convolutional neural networks. For image classification and object recognition, I know that convolutional ...
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28 views

How to construct input dependent convolutional filter?

I am constructing a convolutional variational autoencoder for images, starting out with mnist digits. Typically I would specify convolutional layers in the following way: ...
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How could I convolve a 4D image and a 4D filter with stride?

I want to create a CNN in Python, specifically, only with NumPy, if possible. For optimizing the time of convolution (actually correlation) in the network, I wanna try to use FFT-based convolution. ...
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1answer
50 views

Are mult-adds and FLOPs equivalent?

I am comparing different CNN architectures for edge implementation. Some papers describing architectures refer to mult-adds, like the MobileNet V1 paper, where it is claimed that this net has 569M ...
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How deep convolution filters should be?

In ConvNet, after having decided the number of layers for it, one has to decide the filter size and depth for each layer. The intuition behind the spatial filter size is the number of pixels in the ...
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1answer
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Should image augmentation be applied before or after image resizing?

For the purposes of training a Convolutional Neural Network Classifier, should image augmentation be done before or after resizing the training images? To reduce file size and speed up training time, ...
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147 views

How to identify if 2 faces contain the same person?

I have got numerous frames and I've detected all the faces in all the frames using Retinaface. However I need to track the faces of people over frames. For this purpose, I assumed I could try finding ...
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1answer
43 views

Are there neural networks with 3-dimensional topologies?

The topologies (or architectures) of the neural networks that I have seen so far are only 2-dimensional. So, are there neural networks whose topology is 3-dimensional (i.e. they have a width, height, ...
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30 views

Why does CNN forward pass take longer compared to MLP forward pass? [closed]

Let's take a 32 x 32 x 3 NumPy array and convolve with 10 filters of size 2 x 2 x 3 with stride 2 to produce feature maps of volume 16 x 16 x 10. The total number of operations - 16 * 16 * 10 * 2 * 2 *...
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1answer
48 views

What's a good neural network for this problem?

I am very new to the field of AI so please bear with me. Say there is a dice with three sides, -1,0 and 1, and I want to predict which side it lands on (so only one output is needed I guess). The ...
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29 views

What is the dimension of my output of the form (2n + 1, 2n + 1, #filters) after a MaxPooling layer

I'm trying to white board the different mechanisms behind a convolutional neural network. I have on question regarding the dimension of my volume after using a max pooling layer. Let's suppose I have ...
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How is the data labelled in order to train a region proposal network?

I don't get how the training of the RPN works. From the forward propagation, I have $W \times H \times k$ outputs from the RPN. How is the training data labeled such that I can use the loss function ...
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Stride in convolutional neural networks (horizontal/vertical direction)?

I feel like this question has been asked before, but I can't seem to find any old material on this. In the convolutional layer for CNNs, when you specify the stride of a filter, typical notes show ...
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26 views

Keeping track of multiple faces throughout a video

I have a video where multiple persons are seated. I need to keep track of the emotions they show throughout the video. My final result should be a csv file with all the emotions depicted by each ...
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26 views

Class activation maps for 3D Convolutional neural network?

I have implemented a 3D convolutional neural network and I was not able to find resources for interpretation for my model. I have found some techniques such as GradCam and GradCam++ but these generate ...
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How to combine specific CNN models that work better at slightly different tasks?

I'm not sure how to describe this in the most accurate way but I'll give it a shot. I've developed a Inception-Resnet V2 model for detecting audio signals via spectrogram. It does a pretty good job ...
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Can I use the full dataset for training the CNN feature extractor?

I built a CNN model for extracting features from its dense layer. The extracted features are then used for classification using KNN and Random Forest classifier. My question is, can I use the whole ...
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1answer
30 views

Why is the convolution layer called Conv2D?

When I build a convolution layer for image processing, the filter parameters should have 3 dimensions, (filter_length, filter_width, color_depth) is that correct? ...
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18 views

Object detection using CNN model architectures

I've used LabelImg to create labels for my images using YOLO. After that, I would like to input the images and labels into a CNN model, like a VGG or ResNet. I've searched a lot and have not found ...
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14 views

What's the nearest neighbor algorithm used for upsampling?

Additionally, by default, the UpSampling2D layer will use a nearest neighbor algorithm to fill in the new rows and columns. This has the effect of simply doubling rows and columns, as described and is ...
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Why it is reshaped the last layers of VGG_UNet segmentation model?

I want to do a multiclass segmentation task using deep learning (in python). Here, is a summary of vgg_unet model that is mainly collected from GitHub. So, in my dataset 8 labels are available. So, at ...
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What does “convolve k filters” mean in the AlphaGo paper?

On page 27 of the DeepMind AlphaGo paper appears the following sentence: The first hidden layer zero pads the input into a $23 \times 23$ image, then convolves $k$ filters of kernel size $5 \times 5$ ...
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1answer
32 views

How can I implement 2D CNN filter with channelwise-bound kernel weights?

I would like to bind kernel parameters through channels/feature-maps for each filter. In a conv2d operation, each filter consists of HxWxC parameters I would like to have filters that have HxW ...
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Conditional GAN on harder datasets

I have seen conditional GANs often applied to easier datasets like MNIST and CIFAR-10 to reasonable success, but at the same time these datasets are simple enough that naïve CNNs can fairly easily max ...
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Faster RCNN-RPN Network Training

I am trying to understand RPN network in Faster RCNN. I understand the concept of RPN network, Pass the input images to the pre trained CNN, and get the output as feature maps Make fixed size of the ...
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1answer
39 views

Do all filters of the same convolutional layer need to have the same dimensions and stride?

In Convolutional Neural Networks, do all filters of the same convolutional layer need to have the same dimensions and stride? If they don't, then it would seem the channel produced by each filter ...
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1answer
54 views

What is the time complexity of the upsampling stage of the U-net?

I am trying to determine the complexity of the neural network we use. The neural network is a U-net generator with an input shape of NxN (not an image but image-like data) and output of the same shape....
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1answer
274 views

What is the computational complexity of the forward pass of a convolutional neural network?

How do I determine the computational complexity (big-O notation) of the forward pass of a convolutional neural network? Let's assume for simplicity that we use zero-padding such that the input size ...
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14 views

Limbs for PAFs in OpenPose

What limbs are used in openpose for the PAF? If you look at the skeletal reconstruction one would assume it is $ears - eyes$, $eyes - nose$, $nose-neck$, $neck - hips$, $shoulders - elbows$, $elbows- ...

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