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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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3 views

Will training with yolov4 backup weights early cause weak prediction or is a true backup?

I am training a dataset in yolov4 using the repo from AlexeyAB darknet. In his repo, backup weights are created every so iterations but you originally train with a pretrained weights file. I was ...
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Average loss is 0 when training dataset with darknet yolov4

I am currently training a dataset using yolov4 darknet from AlexeyAB Github found here: https://github.com/AlexeyAB/darknet The dataset I am training is called FishNet Open Images. The dataset has 86,...
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1answer
19 views

Transfer Learning or Custom Network?

I am learning Computer Vision and I was wondering if it's usually worth it to build a custom convolutional network from scratch (through trials and errors) or if using transfer learning with a popular ...
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Grid search optimization [closed]

Can someone suggest some resources (books be best) to learn grid search optimization in CNN? It would be great if any other optimization resources alsos like Bayesian, simple search etc.
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Neural Networks for multivariate regression: can an additional output “help” the training?

I am addressing a problem of multivariate regression by using a CNN. In particular, I have a data set of artificial images which have been generated by a physical model which takes in input, suppose, ...
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How to prevent very large weighted sums [closed]

I'm trying to build a convolutional neural network from scratch to classify handdrawn digits. I'm using leaky RELU as the activation function for the hidden layers, and sigmoid for the final output ...
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Large gap between validation_accuracy and validation_binary_accuracy in Keras

I am building a convolutional neural network in Keras to try to predict binary classification of some text sequences. ...
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9 views

Convolutional Neural Network CNN pixels support the last layer

I'm studying for a computer vision module and I'm on the deep learning topic, in one past paper we have the following question: Given that a convolutional neural network has five convolution layers (...
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Number of additions in Conv2d

I am trying to estimate the number of operations during a forward pass of the Conv2d operator and validate the estimation by using the perf tool. But although I managed to get the right number of ...
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CNN + LSTM model for images performs poorly on validation data set

My training and loss curves look like below and yes, similar graphs have received comments like "Classic overfitting" and I get it. My model looks like below, ...
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9 views

What is the explanation of convLSTM 3x3-256-2?

I have read it in a paper, convLSTM 3x3-256-2 means convLSTM with 3x3 filter size, 256 hidden states, and 2 layers. But the original LINK do not show any argument regarding hidden states and layers. ...
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What is the intuition behind transposed conv layers being able to upscale images?

I was reading the ZF Net paper and it used the term Deconvnet on some searching it seems this is the wrong term and rather we use transposed convolutions instead. I understood how transposed ...
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What should be the input shape for convLSTM if ResNet-50 is applied before?

I have a video dataset, extracted all its frames, and applied ResNet-50 to extract features from all frames. ResNet-50 provides feature map of (2534, 7, 7, 2048), 2534 are the number of frames. Now I ...
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23 views

why trainable parameters are not considered right?

I have tested the "ResNet" block and it works fine,but when i call it in the model class, in some how it does not work properly ? Is it related to the model definition ?
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Train/Validation/Test split and K-fold Cross Validation

I have a dataset that I have split in train, validation and test subsets. I want to evaluate several CNN architectures and hyperparameters so I have trained several models with different ...
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9 views

What should be the input shape for convLSTM if ResNet-50 is applied?

I have a dataset 12 videos. Each video is comprised of 179 frames. On these frames, I have applied ResNet-50 to extract features, and I received (179,7,7,2048) features. As far I know, 179=Total ...
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28 views

What model to use for relative comparison between 3 figures?

I am working on a problem where I am given three images of different dishes (A,B,C) and the task is to figure out if figure B or ...
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23 views

Backpropagation on a CNN

I have tried searching for this, but it seems like no one addresses a key aspect of this problem (or maybe I'm overthinking this): So, first let's assume we have a 3x3 image with a single channel, and ...
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2answers
52 views

High image segmentation metrics after training but poor results in prediction

I'm trying to build a model with Keras that predicts four classes of features from microscopy noisy images which cover about 10 - 30 % of the image. I'm using U-net because my dataset is small (150 ...
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1answer
41 views

Image classification problem using convolutional neural networks [closed]

I am trying to solve this problem by using a convolutional NN to classify an image data set to check the type of disease it is. I have reached task 1b and trying to implement the training loop. ...
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Deep learning detect reference boundary in text (or number of references in text)

I have several documents that either contain or don't an X number of references. I would like to build a model that can detect the number of references if any in a text. I've been thinking for ...
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1answer
61 views

Non Linearity used in LeNet 5

I was looking at the original implementation of LeNet-5 and I noticed a disparity in different sources. Wikipedia suggests that the non linearity used is the same sigmoid in each layer, some blog ...
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How to achieve causal deconvolution with Keras

Keras allows one to specify padding='causal' to achieve autoregressive connections in Conv1D layers. However, in deconvolution with Conv1DTranspose, padding is ...
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Weighting the loss function based on previous seen true positive rates

Similiar to class imbalance there is always something I would call "learnability imbalance" in multi-class classification. What I mean by that: Even when the classes are evenly distributed ...
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1answer
21 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....
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1answer
13 views

Checking trained CNN on the images

I trained my CNN (model) classifier and want to check it on some new images. I have image x, so this syntax works for me for one image: torch.argmax(model(x)) What ...
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16 views

UNet Pytorch Audio Super-Resolution - Upsampling block problems

I am trying to reproduce Audio super-resolution. At the bottom is architecture in PyTorch from this paper (https://github.com/dsgiitr/Audio-Super-Resolution). It is supposed to accept downsamples/...
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35 views

TF/keras implement residual block

I read several papers, where they propose to implement residual blocks of ResNet as follows $$ u^{k+1} = u^k - \tau K^T \sigma(K u^k), $$ where $u^{k}$ denotes output on k-th layer, $\tau$ is ...
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1answer
17 views

Keras: apply multiple filters to each feature map in CNN

I am new to Keras, and I want to do the following: take a 2D image, and apply four 2D convolution kernels to it, giving four 2D feature maps. I could accomplish this. But then I want to apply two ...
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Convolutional neural network with Deep Q learning in games

What does "the average magnitude of maximal action value output by the network" tell us? I mean if we plot this graph, is it good to start as low value and then increase until it goes in a ...
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13 views

Improving a CNNs accuracy - help & advice

So I have created a CNN for image classification, and I train and test it with two datasets. One contains 9,339 images and the other 9,100 images. The first model which I designed gave an accuracy of ...
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Sequence multi-class classification only learns a few outputs

I have a multi-event delineation problem, where given a signal, I have an output with the same signal length. Something like 0011002200, where each unique number ...
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26 views

Validation Accuracy, Validation Loss and Training Loss Remain Constant

Background Hello, I'm new to deep learning and I recently trained a simple convolutional neural network from Francois Chollet's Deep Learning with Python book. The network was trained on 12500 images ...
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1answer
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Why is the kernel of a Convolutional layer a 4D-tensor and not a 3D one?

I am doing my final degree project on Convolutional Networks and trying to understand the explanation shown in Deep Learning book by Ian Goodfellow et al. When defining convolution for 2D images, the ...
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2answers
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Loading pretrained model with Pytorch

I saved my model with this code: from google.colab import files torch.save(net, 'model.pth') # download checkpoint file files.download('model.pth') Then uploaded ...
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Testing trained model on the image from the test set

I trained my EfficientNet (CNN) and got accuracy=0.73. The question is how to check it on one concrete image from the testing set? How to write a code in python for it? I described the testing set ...
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1answer
24 views

Adding Validation PyTorch

First of all, I'm new in this field and it's my first this kind of work. I'm trying to train EfficientNet (CNN), the code below is working fine, but I can't succeed to add also validation set to the ...
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23 views

ROC and AUC curve for CNN multi-class classification problem

I have produced a convolutional neural network to classify images (malware images) into different classes/families. I have managed to produce a confusion matrix and classification report. My ...
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1answer
473 views

ValueError: Classification metrics can't handle a mix of multilabel-indicator and multiclass targets in CNN

I am fairly new to ML and CNN, and this is my first attempt. I have managed to get my model to run, and now I am trying to produce a confusion matrix and classification report, but I am receiving an ...
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20 views

How to interpret stagnant validation curve

I'm new to deep learning, so I'm just learning how to interpret my models. I'm creating a mixed-convolutional neural net to classify melanoma images. Here's the model structure: ...
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7 views

Train only Region Proposal Network in faster RCNN architecture

I am looking for a way to used my pretrained EfficientNetv2 model and turn it into an object detection. Is there anyway, I can put my pretrained model as a backbone and only train the region proposal ...
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20 views

How to use grad cam for ECG data?

I am building a classification model of heart disease from 12-lead ECG data. I would like to implement Grad-CAM (Paper) to visualize the results. Any way to implement Grad-CAM on time series data?
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1answer
29 views

Forecasting with Neural network and understanding which underlying model is favoured

If I have a very large set of data (~ 1TB). How can I use Neural Network on this data to understand which underlying distribution (eg. let's say a Gaussian or a Poissonian with a certain mean, sd) is ...
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1answer
34 views

CNN can't predict images outside the dataset

I am using celeba dataset to train my CNN face landmark detection model. Here is my model ...
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1answer
17 views

Can the performance of a CNN be dependent on the train-test-val split random seed?

I am doing multi-class classification and comparing the effects of 2 image enhancement techniques (IET). IET 1 performs better than IET 2 at random seed x (for train-test-val split) IET 2 performs ...
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1answer
29 views

Does not using more filters in deeper CNN creates more images?

For example, we have applied 32 filters to a single image. Then it created 32 different images (stack of convolutional values). And in the second layer, if we apply 64 filters, are all these filters ...
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1answer
35 views

why transpose convolution is called “transpose”

https://d2l.ai/chapter_computer-vision/transposed-conv.html https://en.wikipedia.org/wiki/Transpose I understand what transpose convolution does, but I am confused about the name of 'transpose'. In ...
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1answer
29 views

Training a neural network with TWO possible correct outputs for one input

I have a system as a black box that has two correct outputs for a single input sample. now I want to train a neural network to generate at least one of the correct outputs for that input sample. what ...
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1answer
38 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 ...
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13 views

Representing multi-channel input signals with a single signal

I am working on an EEG signal classification problem. My dataset consists of EEG signals stored as 19X30000 NumPy arrays. Each row represents a single channel. For now, I am converting each of the ...

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