Questions tagged [convolutional-neural-networks]

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

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Cnn for Combination of both digits and letters(small and capital)

Hi I am new to machine learning can anyone suggest open dataset consists of both digits and letters(small,capital) I want images consisisting of both digits and letters to train my cnn model and make ...
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Coursera CNN week 3 assignment - tensor size 1789? [closed]

In programming assignment number one, in Coursera Convolutional Neural Networks week 3, is an excise which uses YOLO algorithm to perform car detection. Here is the detailed description of the problem....
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What is the difference between same convolution and full convolution in terms of feature map size?

In valid convolution, the size of the output shrinks at each layer. So after some point of time additional layers cannot meaningfully performs convolution. For this reason, same convolution is ...
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Why might the convolution be inappropriate when the task involves incorporating information from very distant locations in the input?

When I am reading about convolutional neural networks, I have encountered the following sentence from the textbook(page 341) that says about the limitation of the usage of the convolution in CNNs. ...
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How to build a word recognizer with text as images using CNN? [closed]

I am new to machine learning. I tried to use this CNN model, which was originally used for handwritten character recognition, for handwritten word recognition, but it is not working. Can anyone share ...
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Is binary classification using CNN possible if the training data only consists of one class?

Is binary classification using CNN possible if the training data only consists of one class? I am working on landslide risk assessment using Convolutional Neural Networks and I want to train a network ...
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Can I get some advices on inferencing people from upwards using Yolov5?

I'm trying to inference people from upwards and count them using Yolov5. I know the controversy between yolov5 and yolov4, but for me, Yolov5 is more easier and reliable to use, also the setup. I have ...
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How does Keras BatchNormalization work?

I have read some articles and watched some videos by Andrew Ng stating that it makes more sense to use batch normalization before applying the activation function. ...
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Origins of the name of convolutional neural networks

Convolutional neural networks (CNNs) contain convolutional layers. In modern deep learning libraries such as Tensorflow and PyTorch among others, convolutional layers are implemented by using the ...
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Should one rescale (normalize) image before or after data augmentation?

During image preprocessing pipeline, should one rescale each pixel value to [0, 1] by dividing 255 first, and then perform data transformation such as color distortion, gaussian blur? or vice versa? I ...
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How to train a model for 1 image class to detect anomaly?

I want to train a model with python over the images, and these images are for a metal product. my aim is to detect the defects, to notice if a product is a failure. what kind of architecture do you ...
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What does the “number of channels” correspond to in U-Net?

I'm studying the U-Net CNN architecture. I'm new to CNNs and am confused regarding the "number of channels". Referring to the U-Net diagram, the input image is convolved with a 3x3 mask ...
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Should I L-2 Normalise outputs in Siamese Neural Neural Network for distance computation for Triplet Loss or not?

I am building a Siamese Neural Network for Images (CNN) which uses the FaceNet's Triplet Loss as its loss function. I found a good Implementation here where we build a model and the outputs from the ...
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What is meant by “real-valued argument” in this context of the convolution operation?

Consider the following statement from Deep Learning book (p. 327, chapter 9: Convolutional Networks) In its most general form, convolution is an operation on two functions of a real-valued argument. ...
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Adversarial Attacks and interpolation methods

I am attacking a model. The model is a simple CNN and PGD is used. The model runs on 112x112 ImageNet dataset. So I first load images as 224x224 and use interpolation function to downsample it to ...
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Why is the F-beta score not increasing while the validation loss fluctuates?

I'm trying to implement a multi-label image classification from a CT scan data set. The goal of the work is to find out which CT scan image has eleven of the most common fractures if it is fractured. ...
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Can anybody just confirm whether or not my understanding of depthwise separable convolutions is correct?

I just need a simple Yes/No confirmation or to debunk my understanding of the difference between the normal convolutions and depthwise seperable convs. I have read quite a few articles and watched a ...
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CNN based model poor result

My goal is to train and evaluate The German Traffic Sign Recognition Benchmark (GTSRB) dataset using Pytorch. I downloaded the datasets from the official site GTSRB_Final_Training_Images.zip and ...
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What does “statistical efficiency” mean in this context?

Consider the following statement(s) from Deep Learning book (p. 333, chapter 9: Convolutional Networks) Convolution is thus dramatically more efficient than dense matrix multiplication in terms of ...
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Any RL approaches for this 2D space optimisation problem?

I have a list of rectangles, they are in certain order in 2D at the beginning. The task is to move them to get the boundary (rectangular) of the minimal area. It's OK to push off the dotted border as ...
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Advantages of CNN vs. LSTM for sequence data like text or log-files

When do you tend to use CNN rather than LSTM (or the other way round) in classification or generation tasks of sequential data like text or log-data? What are the reasons for the decision and what ...
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Expected behavior of adversarial attacks on deep NN?

I am trying adversarial attack (AA) for a simple CNNs. Instead of the clean image, my simple CNN is trained with attacked images as suggested by some papers. As the training goes on, I am not sure if ...
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Showing first layer RGB weights similarly to AlexNet

I would like to show the RGB features learned in the first layer of a convolutional neural network similarly to this visualization of the same layer's features from AlexNet: My learned weights are in ...
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How is a ResNet-50 used for deep feature extraction?

I'm trying to implement the vehicle re-identification model described in https://arxiv.org/pdf/2004.06271.pdf. My question focuses on Section 3.2 of the paper, which uses a ResNet-50 for deep feature ...
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How to add prior information when predicting using deep learning models?

Background I'm building a binary classification model for a pair match problem using CNN, e.g. whether person A1 likes product B1 or not. Model input features are sequence features of the person and ...
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How to fight with unstability in self play?

I'm working on a neural network that plays some board games like reversi or tic-tac-toe (zero-sum games, two players). I'm trying to have one network topology for all the games - I specifically don't ...
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Advice required for identifying bone fragments in CT-scans using STL Files (3D image segmentation)

I am working on a project related to automating the procedure of manually segmenting some bones in CT scans and hopefully if everything goes alright in this stage, move on to do something more with ...
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How to make CNN to recognize whole picture, not just the details?

In my current project I use CNNs to analize plots (CNN autoencoders for feature extraction and KMeans for clusterization) and I get the feeling that these networks, can extract only features that are ...
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What would be the state of the art image captioning deep learning model?

I saw a couple of architectures, like CNN-LSTM, with and without attention model, use of Glove vector, self-critical models, etc. I am overwhelmed looking at different notebooks and architectures, ...
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The Effect of Batch Size in CNN Image Classifier, Given Different Image Domain

I am currently training image classifiers on CelebA face attribute dataset (Binary classifiers), which will be used on images generated by StyleGAN (that is trained on FFHQ256 dataset). These ...
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Must all CNNs and RNNs not have a fully connected layer in order to be considered as such?

In the paper Wrist-worn blood pressure tracking in healthy free-living individuals using neural networks, the authors talk about a combination of feed-forward and recurrent layers, as if FC layers ...
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Is it possible to use RGB image with decimal values when feeding training data to CNN?

I am working with four grayscale images of float32 data type to perform regression using Keras. Three images are stacked using np.dstack to form a RGB data-set. The ...
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1answer
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How to interpret this learning curve of my neural network?

How to interpret the following learning curves? Background: The accuracy starts at 50%, because the network has a binary output (0 or 1). I chose an exponentially decreasing learning rate of the ...
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1answer
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Can residual connections be beneficial when we have a small training dataset?

I have a classification problem, for which an inadequate amount of training data is available. Also, there is no known practical data augmentation approach for this problem (as no unlabelled data is ...
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I want to determine how similar a given song is to Queen's songs. Am I headed in the right direction?

I've asked this question before (@ Reddit) and people suggested CNNs on a mel spectrogram more than anything else. This is great. But I'm sort of stuck at: label some music data as "queen" ...
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Getting loss from each sample in CNN (python) [closed]

I have to plot a cumulative density graph for the performance of my CNN model. But the problem is to do that, I have to get all the losses for each sample in the validation set but it seems like ...
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In a convolutional neural network, how is the error delta propagated between convolutional layers?

I'm coding some stuff for CNNs, just relying on numpy (and scipy just for the convolution operation for pure performance reasons). I've coded a small network consisting of a convolutional layer with ...
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2answers
58 views

Accuracy Not Going Above 30%

I am trying to make a big classification model using the coco2017 dataset. Here is my code: ...
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1answer
56 views

How can Image Caption work?

I have two models and a file contains captions for images. The output of model 1 is .pkl files that contain the features of the images. Model 2 is the language model that will be trained with the ...
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For image preprocessing, is it better to use normalization or standartization?

For a neural network model that classifies images, is it better to use normalization (dividing by 255.0) or using standardization (subtract mean and divide by STD)? When I started learning ...
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Huge dimensionality of input and output — any recommendations?

At work there is an idea of solving a problem with machine learning. I was assigned the task to have a look at this, since I'm quite good at both mathematics and programming. But I'm new to machine ...
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Are there regularisation methods related only to architecture of the CNNs?

Are there any methods of regularisation of deep neural networks, particularly CNNs (or generally ANN but that will also work on CNNs) that are related only to the network's architecture and not the ...
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What are the purposes of pooling in CNNs?

There are three questions on this site related to this What is the effect of using pooling layers in CNNs? Is pooling a kind of dropout? What are the benefits of using max-pooling in convolutional ...
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1answer
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Preparing data set for the YOLO algorithm

Hi I am working on a project which requires the You Only Look Once algorithm in order to classify and localise objects within images. I have to prepare my dataset (which has 2 classes, and predicts 6 ...
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Should one use an “other” category in image classification?

In image classification, there are sometimes images that do not fit in any category. For example, if I build a CNN in Keras to classify Dogs and Cats, does it help (in terms of training time and ...
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1answer
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Which meta-learning approach selection methodology should I use for similarity learning of an image?

Meta-learning has 3 broad approaches: model, metric and optimization-based approach. Each of them has its own sub-approach, like matching network, meta-agonistic and Siamese-based network, and so on. ...
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How a Superpixel Pooling Layer can be used for image segmentation?

The concept of Superpixel Pooling Layer can be found in the paper "Weakly Supervised Semantic Segmentation Using Superpixel Pooling Network". The general idea of superpixel pooling is very ...
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Why are the landmark retrieval and facial recognition literature so divergent?

Context and detail I've been working on a particular image retrieval problem and I've found two popular threads in the literature: Image retrieval (usually benchmarked with landmark retrieval datasets)...
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How to deal with a variable number of channels of the inputs?

I have a problem in which my input data may have a varying number of channels. Let me explain with an example. Imagine we have a classification problem in which we wish to identify if certain species ...
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1answer
47 views

How to normalize images before training?

I have seen people normalize images by just dividing 255. But why? Why not use mean normalization or Z-score Normalization? I also came across this StackOverflow topic while searching but the answers ...

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