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Questions tagged [convnet]

For questions regarding "Convolutional Neural Networks" (CNN)

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Supervisory information through side output in convolutional neural network

I am trying to implement this paper https://ieeexplore.ieee.org/document/7828014 Here they have mentioned text local (edge) and global regions as supervisory information. Side output is generated ...
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Can we do convolutions on binary mask inputs?

I am training a vehicle trajectory prediction algorithm using Deep MaxEnt Inverse Reinforcement Learning (https://arxiv.org/abs/1507.04888). My intention is to have as input to this algorithm a top-...
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Can I use the Softmax function with a binary classification in deep learning?

I want to create a deep learning model (CNN) for binary classification, can I used the softmax function instead of the sigmoid function in binary classification? Adding the classification layer to ...
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How to project a bounding box on feature map?

I'm trying to implement a custom ROI pooling layer in Keras. According to the Fast-RCNN publication, ROI pooling is done this way: RoI max pooling works by dividing the $h \times w$ RoI window into ...
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LeNet-5 - combining feature maps in C3 layer

Famous LeNet-5 architecture looks like this: The output of layer S2 has dimension: 10x10x6 - so basically an image with 6 convultions applied to it to derive features. If each dimension was again ...
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How to train two neural networks together

This could be considered as an extension of my previous question "How to make a region of interest proposal from convolutional feature maps?". Network 1: I have a multi-input neural network, it ...
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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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How to make a region of interest proposal from convolutional feature maps?

Problem Keras does not have any direct implementation of region of interest pooling. I am aware of how to perform maxpooling, but I don't know how to get bounding boxes from feature maps passed from ...
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Creating an image data set from a set of 2D points?

I have N sets of x and their corresponding y coordinates. e.g. x_i = [1.1, 2.3, 3.5] & y_i = [-1.1, -3.2, -5.2]. These coordinates represent an image, which may belong to one of two classes. I ...
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What is exactly meant by neural network that can take different types of input?

There is a scientific document that implements a convolutional neural network to classify 3 different types of data, although how exactly, is unknown to me. Here's the explanation of network ...
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Make image label prediction from Chainer CNN model

I have train dataset of 8000 images and labels. Validation set consists of 1957 images and labels. The test set contains 2487 images. Each image contains White Blood Cell images. WBC is divided innto ...
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Generated training set on convnet

I have a dataset with roughly 800 images that are classified in 18 classes. The classes are spread unevenly, with some classes having 30 images and others having 5. In order to increase my dataset,...
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Does it make sense to train a convolutional neural network on lo-res, use on hi-res pictures?

this is my first machine learning project and actually also my first question here. I am a novice to machine learning with a background in theoretical physics. I want to use a CNN to detect scratches ...
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How can I solve error “allocation exceeds 10 of system memory” on keras?

I made a CNN on Keras with Tensorflow backend, my training set has 144 examples, but each example has size of 3200*101. My CNN is very basic, just for learning, batch_size of 2 (I tried reducing it ...
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1answer
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What features are extracted from pre-trained model of CNN Keras?

I would like to use the CNN pre-trained model in feature extraction but I don't know what features are extracted from that. Please let me know!
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Dataset for GANs (logo generation)

I'm begining work on a new project that involves GANs. So far what I've learnt from some publications (e.g. this) is that these models require literally tonnes of images, e.g. 80K. The problem I'm ...
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Are there real world applications where deep fully connected networks are better suited than ConvNets

I would like to give some brief background for my question to avoid answers that explain the difference between fully connected nets and ConvNets. I completed the first 3 courses in the deep learning ...
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1answer
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Difference between 1x1 Convolution and TimeDistributed(Dense())

Are these lines of code equivalent in Keras? From a few runs, they seem to be, and also intuitively since the channels dimension of my data is 1, my understanding is that a fully connected acts like a ...
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1answer
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Performance of CNN based deep models with number of classes

How does a given deep cnn model performance vary with number of classes in tasks such as classification, object detection segmentation? For example mobilenet v2 gives around 90% accuracy on ...
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CNNs for time series data - concept of channels in Pytorch

This is somewhat related to another post I made Wrangling data for CNN. When inputting an image into a CNN, we have to define a parameter called in_channels in ...
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1answer
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Wrangling data for CNN

I am using convolutional nets for a physics application. I am trying to figure out how to structure my raw data as an image for input into the CNN. I have $N$ samples. Each sample consists of the ...
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Training value neural network AlphaGo style

I have been trying to replicate the results obtained by AlphaGo following their supervise learning protocol. The papers specify that they use a network that has two heads: a value head that predicts ...
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What does the number after LeNet- mean?

I wonder what the numbers 1, 4, 5 in LeNet-1, LeNet-4, LeNet-5 mean. At first I guess that these mean the number of layers with trainable parameters (conv layers, fully-connected layers, and the ...
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1answer
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Question about “1x3 and 3x1 conv is equivalent to 3x3 conv”

I see a lot of sites talk that we can substitute 1x3 conv + 3x1 conv for 3x3 conv. In order to demonstrate easily, we use a 3x3 image as an example. From the point of view of parameters, I know that ...
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What type of neural network should I use to detect meteors in images?

I am currently building a project that takes fisheye images from cameras and detects whether the picture contains a meteor, and if it does it tries to identify where the meteor is. The images look ...
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1answer
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How would you a apply a cnn to do age estimation on static images? [closed]

After doing some reading on age estimation using the IMDB wiki dataset I wanted to try it out myself on a smaller scale but I dont quite understand the application of the CNN. Any clarification would ...
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What kind of layer can do a channel number reduction?

I have a tensor of (1, 1, 1000, 64), i.e. a vector of 1x1000 with depth=64 channels. I'd like to transform this into a vector with a single channel (1, 1, 1000, 1): Using: a ...
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How to increase accuracy of model from tensorflow model zoo?

Situation: My dataset is 70k images of people wearing clothes. Images are labeled: bbox position and class. There are 10 classes. I did 80:20 split. Categories are balanced with exception of one ...
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1answer
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How can I create a pixel labelled image for Semantic Segmentation?

I am following the Semantic Segmentation Examples tutorial by MathWorks. I understand that I can load pixel labeled images ...
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1answer
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CNN backpropagation between layers

I have this CNN architecture: I know how to calculate error for weights based on the output and update weights between output<-->hidden and hidden<-->input layers. The problem is that I have ...
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1answer
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Subsequent convolution layers

Note: I've read How do subsequent convolution layers work? a few times, but it's still difficult to understand because of the parameters $k_1$, $k_2$, and many proposals (1, 2.1, 2.2) in the question. ...
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Encoder Decoder networks with varying image sizes

Encoder Decoder Network - Computerphile : At the very beginning of this video, Michael Pound goes on to say: So it (encoder decoder network) makes no assumptions about the size of the input the ...
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How to train a multi inputs deep learning model using every combination of inputs? [closed]

I am beginner in deep learning. I want to create a multi inputs CNN model in Keras. The model takes two inputs of images to give the two images class. The two images from differnt datasets that have ...
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1answer
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Print the prediction of convolutional neural network

I have designed a convolutional neural network using tensorflow which looks as follows ...
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1answer
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CNN - Is this a Toeplitz Matrix?

I have been reading through Chapter 9 of www.deeplearningbbook.org, where convolutional networks are being described. The following image represents the output of a 2D convolution, without kernel ...
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Why is this convolution equation easier to apply than it's commutative counterpart?

The convolution is an operation on two functions of a real- valued argument. The convolution operation is typically denoted with an asterisk: s(t) = (x ∗ w)(t) ...
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Convolutional neural network for gray images

I am using vgg16 to design a CNN that takes gray input images. The model give me good results without changing anything related to colors. I am not sure if what I did is correct or not. I want to ...
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Deep Learning ROC and Average Precision Curve Results

I used Vgg16 to create a deep learning model and the dataset is imbalanced so, I used class_weight argument in fit_generator method. The model result as the following: accuracy= 98.9% and loss= 0....
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Does CNN take care of zoom in images?

Suppose a convolution neural network is trained on small images of an object, say flower, as in following 3 training images: Will this CNN correctly classify if the same object is present in zoomed ...
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1answer
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Keras ImageDataGenerator.flow_from_directory doesn't find images

I'm working on a deep learning (CNN) problem. I have structured my images into folders correctly (I think), like this: ...
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1answer
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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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1answer
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How to make two parallel convolutional neural networks in Keras?

I created two convolutional neural networks (CNN), and I want to make these networks work in parallel. Each network takes different type of images and they join in the last fully connected layer. ...
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1answer
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Check Overfitting in CNN

I am kind of new to NLP and text classification with Convolutional Neural Nets, and I have trained my first models quite recently. I am a little bit concerned with overfitting. I am doing multilabel ...
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1answer
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Relation between amount of training samples and model depth?

When I add more hidden layers to my CNN (e.g. Dense Layers) it seems that the model needs more training samples to produce good results for classes with few training samples. In the single layer case ...
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CNN kernel location for input image

Given a CNN, say AlexNet: How could one relate kernel locations at the 3rd conv block, i.e 13x13 filter size to the input image. Would that give a meaningful representation in terms of the input ...
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Why is network in network architecture significant?

When talking about convolutions, we have seen networks carrying out the network in network architectures (1x1 convolutions), What is the significance of this process and how does it affect the network ...
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1answer
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How does training a ConvNet with huge number of parameters on a smaller number of images work?

I have two questions: I am wondering why is that a very deep model such as VGG-16 which has approximately 138 million parameters (Source) can be used as a model to be trained on just 1.3 million ...
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1answer
178 views

Shaping data for ConvLSTM for many-to-one image model

Ultimately, I am trying to obtain a binary segmentation mask for an image sequence. I have n number of image sequences, each with 500 greyscale images of size 256px by 400px. Each of these sequences ...
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Keras Conv1D for simple data target prediction

I am trying to use conv1D layer from Keras for predicting Species in iris dataset (which has 4 numeric features and one categorical target). Following is my code: ...
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Too little or too much maxpooling?

I am creating a CNN in Keras where model.summary() shows: ...