Questions tagged [convolutional-neural-network]

A convolutional neural network is a form of neural network with an additional convolutional layer, typically used in image & audio analysis. The convolutional layer is essentially a filtering stage defined by the kernel which is used. For example, a convolutional layer could have a kernel which extracts edges from an image towards the goal of learning which objects are in a scene.

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70 views

Do grouped convolutions actually improve learning?

My Understanding of Grouped Convolutions Let say we have some data with the dimensions [100,100,32] (lets ignore batch size and assume channels last) and we want to ...
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1 answer
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What is `Multi-scale` in Multiscale Convolutional Network?

I was reading an article on Deep Learning and came across this term called Multi-scale Neural Network. I fully understand the concepts of convolutional neural network but it is a bit difficult to ...
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How to use the SHAP library in an existing CNN model

I'm a new student at DL. I want to use the SHAP library (KernelExplainer) to explain an available code using a Conv model which is defined in this path: network.py and then calls the Conv model in ...
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I want to extract only the red and blue lines or enhance these lines in the image provided for easier detection by my CNN model

I am trying to check if the "photo" in the image(some ID card) is forged or not, for that I have done some ELA transformation(found from Kaggle) on the original image which provide me with ...
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Mask R-CNN (matterport) does not generate masks or just generates them randomly

I'm working on a project detecting two different types of olive branches. I'm following this code (based on matterports Mask R-CNN) with my own dataset: https://github.com/AarohiSingla/Mask-RCNN-on-...
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Classifying Hot Drinks

I have a university project in which I will attempt to build a machine learning algorithm to classify images of hot drinks (e.g tea, coffee, etc) and I was wondering about the best approach to do this....
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1 answer
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What is the best way of combining audio and visual data to make predictions?

I am trying to predict the probability of a disease by using audio and images, the audio and the images do not come from the same source. I am thinking of combining the outputs (maybe average them) of ...
4 votes
3 answers
8k views

How many parameters in a Conv2d Layer?

I was following andrew-ng coursera course on deep learning and there's a question that has been asked there which I couldn't figure out the answer for? Suppose your input is a 300 by 300 color (RGB) ...
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1 answer
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Loss function for a regression model in image segmentation task

I am training a model to segment an image to predict the degree of damage (ranging from 0: no damage, to 5: severe damage) for each pixel of an image. I have approached it this way: ...
5 votes
1 answer
3k views

Gradient flow through concatenation operation

I need help in understanding the gradient flow through a concatenation operation. I'm implementing a network (mostly a CNN) which has a concatenation operation (in pytorch). The network is defined ...
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Bending Training Loss, what could be the cause?

Hello, during training of one of my models, I observe the following training (blue) and test (orange) loss patterns. At first, the training loss increases, then bends and starts decreasing. Just ...
3 votes
2 answers
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ConvNet with concatenated data

I have a basic question regarding convolutional neural network. Assume I have a set of 1000 RGB images and I train a CNN from this set. I can obviously split each of my RGB images into 3 different ...
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GNN Model - Analyzing Training Curve

Introduction. Actually, I am working on a Graph Neural Network (GNN) model to predict some graph-level float values. So, input=graph, output=float predicted value. I trained and evaluated the proposed ...
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1 answer
651 views

Setting input shape for an NLP task in R(Rstudio) using keras 1D convolution layer, when it expects 3 dimensional input (a tensor)

I am using R programming language and using Keras API to build a functional 1D CNN. I have a matrix of my dataset of the following shape rows*features (6000*1024). The input layer is set using the ...
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3 answers
148 views

Problem with overfitting

I make small CNN from scratch to classify barcodes. I have two classes: one for images with barcodes and second for all what isn't barcodes (items, animals, landscape, furniture, people). I got good ...
3 votes
1 answer
281 views

Why is training and validation loss steadily rising (eventually to NaN) in this CNN of mine?

Dear ML and data scientists: I have 4 layers of gray scale images for every single biological specimen in my dataset. I am trying to train a 4-convolution CNN (see pytorch architecture below) to ...
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Siamese Network constant accuracy with more training data

I am new to machine learning and I am currently trying to create a siamese network that can predict the similarity of brand logos. I have a dataset with ~210.000 brand logos. The CNN for the siamese ...
5 votes
1 answer
1k views

How to use PCA in CNN for image recognition using Keras?

I created a CNN model for image classification and I want to use Principal Component Analysis (PCA) but when I run pca.fit() code, the code still running for hours ...
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1 answer
221 views

Tensorflow Conv3D with variable input size

I have a hypotethical question: Is it possible to train Conv3D with variable input size? Sample dim = Length x Width x Depth ; Depth are fixed per each samples, let's say 500. However Length x Width ...
1 vote
1 answer
202 views

Correct way of computing dice score for image segmentation?

In binary image segmentation, for given a set of images, it's true mask and predicted mask. How do you compute dice score? Should I compute the dice score for each image separately and then find mean ...
1 vote
1 answer
68 views

Magnification factor in image classification

If a CNN is trained on images focusing on an object, will it also recognize when multiple such objects are present in the image? For example can a network trained on single flower images also ...
2 votes
1 answer
159 views

Doing a fine tuning after a transfer learning

I read about fine tuning and transfer learning for CNNs and was wondering if we can do fine tuning after using transfer learning on the same CNN? If so, will this increase the performance of the model ...
3 votes
1 answer
1k views

Keras bug NasNetlarge no top

I am trying to use NasNetlarge in Keras without the top but I cant get rid of the top: ...
4 votes
2 answers
719 views

How to determine the number of the training images in Keras after data augmentaion?

I want to create a CNN model and I am using data augmentation. I want know the number of augmented images in Keras. How to determine the number of the training images in Keras after data augmentation?...
1 vote
2 answers
463 views

What is preferred upsampling or zero padding?

When training a CNN one option is either to zero pad an image to make it bigger or upsample it. When should I choose each one? What criteria is leveraged for choosing a method?
2 votes
2 answers
4k views

How to properly save and load an intermediate model in Keras?

I'm working with a model that involves 3 stages of 'nesting' of models in Keras. Conceptually the first is a transfer learning CNN model, for example MobileNetV2. (Model 1) This is then wrapped by a ...
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How to see Latency at layer granularity in a CNN

I am finding documents or an example that measure Latency at layer granularity in the AlexNet model. Please could share or tutorial for me.
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2 answers
682 views

Problems with shape of Conv1D on Keras

I have some problems with layers construction on Keras. I explain the whole problem: I have a feature matrix, with dimensions: 2023 (rows) x 65 (features); I tried to build a CNN, with Conv1D as ...
1 vote
1 answer
29 views

Fine tuning Convolutional Neural Network with a learnable first layer

I have a classification task using grayscale images and I want to leverage from pretrained networks. There are a lot of resources out there presenting how to fine tune large neural nets like resnet, ...
1 vote
1 answer
22 views

Found input variables with inconsistent numbers of samples: [908, 9080]

I have a dataset, I have reconfigured my tensors as a single 3072 sized line array. I have reconfigured the valid dataset and training dataset. You can find all of the information about my train, ...
0 votes
2 answers
769 views

Keras Conv1D model Input_shape value error

I am not sure why I am receiving this value error. Additionally, I haven't found a tutorial that explicitly talks about the appropriateness of size of filters and kernel. I would appreciate some input ...
2 votes
1 answer
443 views

Why is convnet transfer learning taking so long?

I am using transfer learning to train a binary image classification model using keras' pretrained VGG16 model. The code can be found below : ...
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1 answer
63 views

Why does neural network need loss as scalar?

I have a loss function that's a weighted cross entropy loss for binary classification ...
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0 answers
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Different Kernel Initializers in my prediction layer with Transfer Learning could affect performance?

So I have this model right here and the task is to classify 3 labels.: ...
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1 answer
156 views

Varying Image sizes in Tensorflow Malaria dataset | Dealing with unclean tensorflow data

I am trying to build a CNN based image recognition system for the Tensorflow malaria dataset. I loaded the dataset (~27k RGB images) using conventional tensorflow_datasets syntax. After some data ...
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Pytorch CNN in_channels, out_channels for Classifying DNA Sequences

Apologies as this question has been asked before -- I'm really trying to wrap my head around the motivation behind designing neural network architecture. I'm designing a convolutional neural network ...
1 vote
1 answer
57 views

One hot vector output in classification task

I'm working on CNN model and I used one hot vector type of labels. The number of classes is 3: [1,0,0], [0,1,0], [0,0,1]. net(x) I'm getting such an output: [0....
1 vote
1 answer
207 views

Reason for Training and test loss sudden increment after some epochs keras

We know that if training and test loss are different from each other, our model is over-fitting. However, if both get high after some epochs, how can we justify it? One way to solve it is to reduce ...
1 vote
1 answer
333 views

How to deal with severe overfitting in a UNet Encoder/Decoder CNN in a task very similar to image translation?

I am trying to fit a UNet CNN to a task very similar to image to image translation. The input to the network is a binary matrix of size (64,256) and the output is of size (64,32). The columns ...
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14 views

Implementing the backward step for conv2d layers

I am trying to recrate the conv2d layers using the eigen library but I have some problem understanding how the backward step for conv2d layers is calculated exactly. Before I go into explaining my ...
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1 answer
35 views

Transpose Convolution feature extraction

Convolution extracts high-level features, but what about Transpose Convolution (or De/Up-Convolution)? Does it behave exactly the opposite? Does it generate lower-level features?
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Using CNN to extract channel network from old maps

I am a numerical modeller working on a flow problem. I have developed a channel network simulator to model fluid flow through an irrigation network. As part of my inputs I have to use old maps (as old ...
1 vote
1 answer
32 views

Which representation of CNN feature maps is correct?

When I extract my features from my CNN, it doesn't look like this: And those pictures are not just representation. From this article it can be seen that these features are actual extracted features ...
1 vote
1 answer
85 views

Why would the accuracy of a model change when the loss doesn't?

I've trained 8 models based on the same architecture (convolutional neural network), and each uses a different data augmentation method. The accuracy of the models fluctuates greatly while the loss ...
3 votes
3 answers
3k views

Can pooling ever increase accuracy in convolutional neural networks?

In ConvNets, pooling is used to downsize the input volume, leading to fewer parameters, leading to computational efficiency and possibly helping with overfitting. But can pooling ever increase the ...
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1 answer
181 views

Working of Dense Layer

What kind of operation does Dense Layer perform to reduce dimemsion. So basically I have used Dense layer to compress the dimension all the time like from 10000 neurons to direct 2000 neurons or even ...
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Style Transfer - how to choose the cnn layers?

I read this tutorial. In that tutorial they choosed: conv layer #4 for content_layers conv layers: 1, 2, 3, 4, 5 for ...
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What happens in a transposed convolution when the stride is bigger than the kernel width/height?

In Pytorch's transposed convolution API, you can specify a stride that is larger than the kernel_size. For example: Input image of size 2x2 Kernel of size 2x2 ...
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29 views

Dealing with variable length videos where frames are multi class in Keras' temporal convolutional networks (TCN)

I am working on an action segmentation problem whereby I have multiple videos of different lengths containing several actions. I have created features for each video which are also of variable length. ...
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5 views

How to add linear metadata to a 2D convolutional layer

I have an autoencoder which takes a 2D image as input and outputs a 2D image. The architecture is currently a set of 2D convolutions and RelU layers. I would like to add a ~10 element linear input ...

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