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Questions tagged [convolutional-neural-networks]

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

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Pseudocode for CNN with Bounding Box and Classifier

I've been looking at various bounding box algorithms, like the three versions of RCNN, SSD and YOLO, and I have noticed that not even the original papers include pseudocode for their algorithms. I ...
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What is the right way to convolve over word embeddings?

I have two word embeddings w1 and w2 with dimension 100 as input into my convolutional neural network. It should learn the similarity between these two words. I am now concerned with the applied ...
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Predicting global horizontal irradiance using satellite images [migrated]

I have the aim to build a model to predict global horizontal irradiance (ghi) using satellite images and other features namely the day of the year and time of the day. For extracting the satellite ...
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Mnist CNN Architecture

In this tutorial from Jeremy Howard: What is torch.nn really? he has an example towards the end where he creates a CNN for mnist. In nn.Conv2d he makes the ...
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How do I assign a matrix of data as a label to each input image in my dataset using PyTorch? [migrated]

I want to train a convolutional neural network (CNN) in PyTorch to predict frequency spectrum data related to an input image. Rather than assigning one label to each image (Dog, Cat, Car, Airplane, ...
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Feature visualization on neural networks which are not for classification

Feature visualization allows to better understand neural networks by generating images that maximize the activation of a specific neuron, and therefore understand what are the abstract features that ...
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Any Case that Inception V1 (GoogleNet) Performs Better than Inception V3?

I tried to do transfer learning using 2 methods : GoogleNet with Caffe framework training on Nvidia DIGITS Inception V3 with Tensorflow framework (no DIGITS) My dataset is quite large with 1 million ...
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How to use CNN for making predictions on non-image data?

I have a dataset which I have loaded as a data frame in Python. It consists of 21392 rows (the data instances, each row is one sample) and 1972 columns (the features). The last column i.e. column 1972 ...
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How to determine the minimum scale for detectable objects in a object-detection architecture?

Note: I think the title is a bit too generic, so I'm open to suggestions on how to improve it. I'm currently working with Mask RCNN, which does instance segmentation, but I believe the question is ...
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How to train CNN such it eliminate dependent features and focuses on independent ones?

How we should train a CNN model when training dataset contains only limited number of cases, and the trained model is supposed to predict class (label) for several other cases, which has not seen ...
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Sample from a distribution inside a NN layer

Is it possible to sample from a distribution inside a neural network forward function? Assume that there is a NN and a sample is needed to be derived from it at every forward-pass to randomly set a ...
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42 views

Reduce receptive field size of CNN while keeping its capacity?

I have a convolutional encoder (a CNN) consisting of DenseBlocks and a total of 50 layers (cf. FC-DenseNet103). The receptive field of the encoder (after last layer) is 660 according to Tensorflow ...
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Constant in Loss Function of Style Transfer

After I read paper by Gatys, Image Style Transfer Using Convolutional Neural Networks, I notice there aren't any explanations for the constant in Eq. (4): $$E_l = \frac{1}{4N_l^2M_l^2}\sum_{i,j}(G_{...
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54 views

Using GAN's to generate dataset for CNN training

I'm doing bachaleor thesis on traffic sign detection using single shot detector called YOLO. These single shot detectors can perform detection of objects in image and so they have specific way of ...
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How can VAE have near perfect reconstruction but still output junk when using random noise input

I am creating a VAE for time series data using CNNs. The data has 4800 timesteps and 4 features. It is standardized and normalized. The network I am using is implemented in Keras as follows. I have ...
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Choosing the right neural network settings

I'm trying to train a neural network on evaluating chess positions if rather white (0.0) or black would win (1.0) Currently the input consists of 4 bits per chess field (piece id 0 - 12, equals 64*4)....
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YOLO: How are the outputs created and how are feature maps used?

I've been looking into YOLO algorithm and couldn't understand how the final output is made. It seems that training YOLO requires the following information: Grids that are divided into a size of S x ...
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1answer
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Testing, Validation Percentage & Test, Validation Batch Size Difference?

I'm doing transfer learning using Inception on Tensorflow. The code that I used for training is https://raw.githubusercontent.com/tensorflow/hub/master/examples/image_retraining/retrain.py If you ...
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How does a neural network output text box location data?

I'm interested in creating a convolutional neural network or LSTM to locate text in an image. I don't want to OCR the text yet, just find the text regions. Yes, I know Tesseract and other systems can ...
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1answer
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Using batches in testing

If one examines SSD: Single Shot MultiBox Detector code from GitHub repository, it can be seen that, for a testing phase (evaluating network on test data set), there is a parameter ...
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2answers
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What's the role of bounding boxes in object detection?

I'm quite new to the field of computer vision and was wondering what are the purposes of having the boundary boxes in object detection. Obviously, it shows where the detected object is, and using a ...
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Tensorflow : Inception V3 Transfer Learning Parameter Tuning

Sorry if my question is at the wrong place, I'm new in this community. So, I have dataset with total of 1 million images (augmented) that separated in 28 classes. I followed this tutorial https://www....
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Keras simple CNN not learning

I was trying to write a simple CNN in keras during a course, and I wrote one that does not learn at all, but I don't understand why. Don't bother about the coding, first I load two images of a dog ...
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1answer
82 views

Additive Attention in Convolutional Networks

Attention has been used widely in recurrent networks to weight feature representations learned by the model. This is not a trivial task since recurrent networks have a hidden state that captures ...
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1answer
23 views

Pixel-Level Detection of Each Object of the Same Class In an Image

I have source data that can be represented as a 2D image of many similar curves. They may oftentimes cross over one another, so regions of interest will overlap. My goal is to implement a neural ...
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71 views

CNN-feature extraction

Is there any way to control the extraction of features?How to recognize what features are been learnt during training i.e relevant information is been learnt or not?
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Can machine learning algorithms (CNNs?) be used/trained to differentiate between small differences in details between images?

I was wondering if machine learning algorithms (CNNs?) can be used/trained to differentiate between small differences in details between images (such as slight differences in shades of red or other ...
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How does DARTS compare to ENAS?

How does DARTS compare to ENAS? Which one is better or what advantages does they each have? Links: DARTS: Differentiable Architecture Search Efficient Neural Architecture Search via Parameter ...
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How data augmentation like rotation affects the quality of detection?

I'm using an object detection neural network and I employ data augmentation to increase a little my small dataset. More specifically I do rotation, translation, mirroring and rescaling. I notice that ...
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2answers
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Learning Rotated bounding box for object detection

I have checked out many methods and paper like yolo, ssd, etc with very promising result in detecting a rectangular box around object, But could not find any paper, which shows an learning a rotated ...
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1answer
56 views

Measuring Width of Crack

Are there any projects where you can detect and measure the width of a crack? I am using tensorflow and labeling the data sets for now.
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Dice loss gives binary output whereas binary crossentropy produces probability output map

On recommendation of Kanak on stackoverflow I am posting this question here: Currently I am experimenting with various loss functions and optimizers for my binary image segmentation problem. The loss ...
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3answers
37 views

Dimension after multiple convolutions in ConvNets

I'm trying to understand exactly what does a convnet do to what, and I have trouble finding the dimensions alongside the convolutions. If we take VGG 16 architecture, how do I get from 224x224x3 to ...
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Why do we throw out negative ReLU value?

when using Rectified Linear Unit after convolution layers we have to have twice as much filters to be able to detect features (eg both left and right edge detector). Why do we just throw out negative ...
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Understanding CNN || Step - > Convolution Followed by RELU

This might sound dumb but i kept scratching my head for long and couldn't understand the non linearity concept. Let's say i have a 2 x 2 pixel of grayscale picture where there is one edge such that ...
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1answer
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Smaller interest area for images than the size of the image in classification neural networks

I have the following binary classification problem, my labeled dataset contains images 96x96 px. Now in every image the interest area is of size 32x32 px in the center of the image, and the images are ...
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What is the concept of channels in CNNs?

I am trying to understand what channels mean in convolutional neural networks. When working with grayscale and colored images, I understand that the number of channels is set to 1 and 3 (in the first ...
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1answer
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Appropriateness of 3D Convolutional Neural Network for segmentation of medical image data

I have a couple different segmentation tasks that I would like to perform on medical imaging data using CNN's. I'm currently trying to wrap my head around how well a 3D network might work, using a U-...
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Image Segmentation Prediction with cropping 256x256 grids is very slow

I have only a limited dataset (<25) with large-sized images (>1500x2000) and their pixelwise labels. The aim is to find unusual patterns in this industry dataset and highlight them. To generate ...
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1answer
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Relationship between input range and channel means, standard deviations for CNNs

So, I'm using a pretrained pnasnet5large model to do some image classification (https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py) In the file, it ...
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2answers
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Is pooling a kind of DropOut

If I got well the global idea of DropOut it allows to improve the sparsity of the information that come from one layer to another by setting some weights to zero. In another hand, pooling, let's say ...
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Pre trained model for low resolution image! How to handle?

What's the strategy if the resolution of an image is very low such as 28 x 28 or 100 x 100 or 150 x 150, for transfer learning? Pre-trained models such as Inception, Xception, VGG-16 etc are required ...
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77 views

How to approach this handwritten digit recognition?

I have multiple pictures that look exactly like the one below this text. I'm trying to train CNN to read the digits for me. Problem is isolating the digits. They ...
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1answer
46 views

Recognition of small objects

I'm currently implementing an Android app for street sign recognition. My solution works quite well for the GTSRB dataset, since it provides a labeled test set of centered images. However, it doesn't ...
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1answer
49 views

How to get a binary output from a Siamese Neural Network

I'm trying to train a Siamese network to check if two images are similar. My implementation is based on this. I find the Euclidian distance of the feature vectors(the final flattened layer of my CNN) ...
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2answers
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Keras giving memory allocation error and running extremely slow

I am working on character recognition using convolutional neural networks. I have 9 layer model and 19990 training data and 4470 test data. But when I am using keras with Tensorflow backend. When I ...
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1answer
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How to solve the AttributeError: 'Ssd' object has no attribute 'freeze_batchnorm' [closed]

I use a modified training script for modeling images with Tensorflow/Keras/Mobilenet_V2. After a few errors that I could solve I now get the following error: ...
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How do randomly initialized neural networks behave?

I am wondering how the output of randomly initialized MLPs and ConvNets behave with respect to their inputs. Can anyone point to some analysis or explanation of this? I am curious about this because ...
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Can the same input for a plain neural network be used for a convolutional neural network?

Can the same input for a plain neural network be used for CNNs? Or does the input matrix need to be structured in a different way for CNNs compared to regular NNs?
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How a game playing agent could identify potential objects and proximity?

Most implementations I'm seeing for playing games like Atari (usually similar to DeepMind's work using DQN) have 4 graphical frames of input fed into 3 convolutional layers which are then fed into a ...