Questions tagged [convolutional-neural-networks]

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

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101 views

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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1answer
101 views

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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0answers
35 views

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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2answers
30 views

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
1k views

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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0answers
167 views

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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2answers
151 views

Keras simple CNN not learning [closed]

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
341 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
37 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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3answers
104 views

How do I determine which relevant features have been learned during training in a CNN?

Is there any way to control the extraction of features? How do I determine which features are been learned during training, i.e relevant information is been learned or not?
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2answers
156 views

Can machine learning algorithms be used 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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0answers
209 views

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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137 views

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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5k views

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
68 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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1answer
559 views

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
129 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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1answer
216 views

Is there a ReLU-like activation function that concatenates positive and negative values?

Is there a ReLU-like activation function that concatenates positive and negative values? What is its name? Apparently, it doubles the output dimension.
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1answer
144 views

Understanding the application of Sobel kernel followed by ReLU to a zero-padded image

Let's say I have a $2 \times 2$ pixel of grayscale picture, where there is one edge such that the left pixel contains a value, 30, and the right pixels contain a value 0 (in red below). And for edge ...
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1answer
35 views

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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2answers
7k views

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 ...
2
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1answer
85 views

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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0answers
55 views

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
55 views

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
1k views

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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2answers
119 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
80 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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2answers
465 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
432 views

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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2answers
130 views

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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2answers
86 views

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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44 views

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 ...
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1answer
72 views

Can I do oversampling by copying the same image multiple times? Will it effect my neural network accuracy?

I am working on an image data-set. As you may have guessed it is imbalanced data. I have 'Class A, 19,000 images' and 'Class B, 2,876 images'. So I did an undersampling by removing randomly from the ...
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32 views

How to preprocess a modified dataset so that a fitted CNN makes correct predictions on an un-modified version of the dataset?

for a school project I have been given a dataset containing images of plants and weeds. The goal is to detect when there is a weed in the pictures. The training and validation sets have already been ...
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1answer
213 views

Most efficient neural network for human activity recognition

A paper from machinelearningmastery.com on human activity recognition states that 1D convolutional neural networks work the best on classification of human activities using data from accelometer. But, ...
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1answer
76 views

Are commercially available neural ICs digital?

Apparently, one can buy a special-purpose integrated circuit (an IC like this one, for instance) to host a convolutional neural network. QUESTION Is such a circuit digital? Except for digital random-...
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1answer
30 views

Image prediction model when data-set classes have visual similarity

Lets say we have a data-set of all cats and we have to identify the cat breed based on given test image. As, the two different cat breeds have visual similarity can we use existing networks (VGG, ...
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62 views

Machine learning approach to facial recognition

First of all I'm very new to the field. Maybe my question is a bit too naive or even trivial... I'm currently trying to understand how can I go about recognizing different faces. Here is what I ...
2
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1answer
115 views

Why does the number of feature maps increases in the VGG model?

I found the below image of how a CNN works But I don't really understand it. I think I do understand CNNs, but I find this diagram very confusing. My simplified understanding: Features are ...
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3answers
98 views

Features Map convolutional neural network

I have a question about convolutional neural newtork. Consider this image: conv example We have a part of an input matrix and a filter. Ok, now we can do the convolution and the result is a scalar, ...
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1answer
235 views

What should a good loss curve look like?

This is a very basic question. I'm running a faster rcnn trainer on a dataset for object recognition. My images range from 200x200 to 7360x4912 in resolution. There are only 2 classes being trained (...
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1answer
29 views

Problem extracting features from convolutional layer where the dimensions are big for feature maps

I have trained a convolutional neural network on images to detect emotions. Now I need to use the same network to extract features from the images and use the features to train an LSTM. The problem is:...
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2answers
3k views

What is the difference between a receptive field and a feature map?

In a CNN, the receptive field is the portion of the image used to compute the filter's output. But one filter's output (which is also called a "feature map") is the next filter's input. What's the ...
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100 views

Multi-channel CNNs and channels

In language models, CNNs can extract different n-gram features from the input. From my current understanding, these models are called "multi-channel CNNs". I'm referring to these materials: https://...
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0answers
50 views

Using features extracted from a CNN as convolutional filter

I'm a bit confused about this. Assume I have a CNN network with two branches: Top Bottom The top branch outputs a feature vector of shape 1x1x1x10 (batch, h, w, c) The bottom branch outputs a ...
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1answer
2k views

How to combine input from different types of data sources?

I've to train a neural network using microphone data (wav files), accelerometer sensor data and light sensor data. Right now the approach I thought was to convert all data into images and combine ...
2
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1answer
64 views

How to measure the reasoning capabilities of neural networks

Which possibilities exist to evaluate the visual reasoning capabilities of neural networks in the field of image recognition? Are there methods to measure the ability of machine reasoning? Or ...
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0answers
71 views

neural network deconvolution filters

I understand the concept of convolution. Let's say that my input dimension is 3 x 10 x 10 And if I say that I will have 20 activation maps and a filter size of 5, ...
3
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1answer
189 views

Best way to create an image dataset for CNN

I am creating a dataset made of many images which are created by preprocessing a long time series. Each image is an array of (128,128) and the there are four classes. I would like to build a dataset ...
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3answers
729 views

Neural Network for Optical Mark Recognition?

I've created a neural net using the ConvNetSharp library which has 3 fully connected hidden layers. The first having 35 neurons and the other two having 25 neurons each, each layer with a ReLU layer ...

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