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Questions tagged [cnn]

Convolutional Neural Networks (CNN, also called ConvNets) are a tool used for classification tasks and image recognition. The name giving first step is the extraction of features from the input data.

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1answer
14 views

Convolutional neural networks for non image dataset

Can we use Convolutional Neural networks for a non image dataset for prediction? The dataset is a record of student academic details I know that CNN is mostly used in computer vision and image ...
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1answer
12 views

Siamese networks vs Semantic similarity (may be gensim)

I am trying to understand the Siamese networks . In this vector is calculated for an object (say an image) and a distance metric is applied (say manhatten) on two vectors produced by the neural ...
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Input BOW in CNN-model

I am having dataset having 78665 rows (sentences) 128 features/columns (characters in sentence including padding) I want to give this as an input a CNN but it is giving errors, the code is below. ...
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Does the type of y value affect the prediction power of model?

I am using the sunlight intensity time series data(X) to predict plant height(Y) in different locations using CNN model in Keras. I am wondering if I change the group Y values into 2 categories: High ...
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Removing layers from a convolutional encoding-decoding network

I've been reading this paper on Style-transfer (Universal Style Transfer via Feature Transforms): A crucial part of the algorithm (Section 3.3) uses a pre-trained VGG-19 network as an encoder to ...
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2answers
118 views

How prevalent is `C/C++` in machine learning development?

I am currently a data scientist mostly doing NLP, and I do most of my work inPython. Since I didn't get a CS degree in undergrad, I've been limited to very high ...
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1answer
14 views

Transfer learning VGG16 does not work as expected. (Detect tacos as hamburgers)

I am new in this field of machine learning, to test I wanted to do a simple project. Create a cnn capable of recognizing hamburger images. As I do not have the ability to collect more than 10,000 ...
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0answers
19 views

How do I compare more than 20 deep learning models?

I have to compare several deep learning models (CNNs) based on the same dataset. For estimating the model skill's I use the train_test_split instead of ...
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1answer
24 views

Error on custom RNN/LSTM with multiple inputs

I want to implement a custom RNN/LSTM model similar to this. The model should take two separate vectors as input and process them. I was following keras tutorial to implement a custom keras layer and ...
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0answers
29 views

Multilabel Classification; which network design?

I have a hard time thinking about how I can build this network with the following problem: I want to build a CNN to classify notes from sheet music. I have tried several models with and without ...
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1answer
20 views

Can I save only some VGG19's layers into a .H5 file?

I am training a deep-learning style transfer model with the pretrained-VGG19 CNN. My aim is to use it in my Android app for personal purposes with Google Firebase Machine Learning Kit (which would ...
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0answers
26 views

How is the output of a maxpool layer window size=1x2 and stride=2 calculated?

I'm looking at the architecture proposed in the following paper: Baoguang Shi et al, An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text ...
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1answer
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Keras ValueError: Error when checking model target

The algorithm is designed to describe products (clothes). This part recognizes the colors of clothes (14 output values). First i want to build some simple output model (EfficientNetB5) part of the ...
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0answers
20 views

Detect the important part of a receipt (CNN)

In general I want to detect prices and products from a receipt. My approach was to detect the important part of the receipt (products with their prices) to then afterwards pass to firebase ml kit to ...
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1answer
22 views

K-fold-cross-validation if training dataset is much smaller than test dataset?

I'm a beginner in machine learning and I have a special case in which I have only a small training dataset of about 500 images and a test dataset of 10,000 images. Does it still make sense to do a 10-...
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1answer
23 views

PyTorch CNN network outputs homogeneous results

I am a beginner at data science and I got a project where I want to do nlp via a convolutional neural network in PyTorch. The problem is that regardless of what comes out of the convolutional layers, ...
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Issues with YOLO training

I want to detect plates using on this dataset, I follow this tutorial But an error generated in the beginnig of the training: Any idea how to fix this?
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11 views

GradCAM heatmap all negative

I've fully trained the VGG16 model on my dataset, resulting in a 97% validation accuracy. I'm using the code from this github: https://github.com/PowerOfCreation/keras-grad-cam but for 2 of my 16 ...
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0answers
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What is the use of having shared weights in later layers of a CNN?

In a CNN, all the neurons in a single layer use the same weights and bias. As a result, all the neurons detect the same feature. The early layers of a CNN detect simple features like edges and hence ...
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1answer
40 views

Are CNNs indeed translation invariant?

I read that in a hidden layer of a convolutional neural network, all neurons share the same weights and bias. As a result, all the neurons detect the same feature and hence ConvNets become invariant ...
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1answer
14 views

How to perform preprocessing for hyperspectral images

Here I would like to apply the CNN and DNN for face recognition computing and later will compare that both of them which one is better or faster My idea is - In the UWA hyperspectral face database, ...
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0answers
8 views

Number of parameters in a 3D U-Net

I am interested in the 3D U-Net as proposed in Çiçek et al. 2016 (https://arxiv.org/abs/1606.06650). A Caffe implementation is publicly available (in particular, the prototxt), but I am using ...
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11 views

Does a CNN fully memorize ground truth if it has more parameters than training pixels?

ResNet consists of 25M trainable parameters. If only 30% of 600 $512 \times 512$ images is annotated, there are $600 * 512 * 512 * ~0.3 = 47,185,920$ ground truth pixels. A parameter is a floating ...
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1answer
33 views

CNN always predicts either 0 or 1 for binary classification

I am using a Kaggle dataset on stress characteristics, derived from ECG signals, and I would like to train a CNN to recognize stress/non-stress situations. I have built a model in Keras: ...
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0answers
17 views

Spatial and temporal information processing together (CNN and LSTM)

I have small problem that requires to process both spatial and temporal information. I need to predict vehicle's trajectory based on previous trajectory information and map information. My current ...
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1answer
54 views

X_train, y_train from ImageDataGenerator (Keras)

Can I have X_train, y_train, X_test, y_test from data_generator? Here is my code: ...
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1answer
24 views

How CNN applies backpropagation to update its weights and biases?

I understand that the 3 main layers for CNN are convolutional layer, ReLU layer and pooling layer. However, I do not understand how CNN updates its weights and biases using backpropagation. I ...
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21 views

Using CNN for regression

I have a question about using CNN for regression. My problem is the following. I have a set of coordinates, which represent positions of goods in a warehouse that have to be picked: pick point=(x,y). ...
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1answer
16 views

Training CNN for Regression

Background: I am using CNN to predict forces acting on a circular particle in a granular medium. Based on the magnitude of the forces, particle exhibits different patterns on its surface. The images ...
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1answer
19 views

Why do we need convolutions over volume in convolutional neural networks for image recognition?

In convolutional neural networks, we make convolutions of three channels (red, green, blue) with a filter of dimensions $k\times k\times 3$, like in the picture: Each filter consists of adjustable ...
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21 views

Issues with Implementation of CNN based on Paper

I am attempting to duplicate a CNN in a paper, and am having issues with bad accuracy and loss not decreasing past 40. As described in this paper, specifically on pages 6/7, the network architecture ...
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23 views

CNN how can i reduce gpu memory usage with large image sizes?

I am trying to train a cnn-lstm model, my sample image sizes are 640x640. I have a GTX 1080 ti 11GB. I am using Keras with tensorflow backend. Here is the model. ...
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1answer
26 views

Classifying Letters using CNN - Help

so some context, I'm trying to develop an OCR (for fun) and for that reason I decided to first find text within a page, parse it in to letters within the text and from there try and classify the ...
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what will happen if by mistake i train a object detection algorithm with images containing multiple bounding box in the same object?

I have a dataset of images where I may have some images where the bounding box is annotated time on the same object. Will that create a problem in the accuracy of the model?
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18 views

How to avoid different accuracies when training with subsets?

when trying to train a CNN with randomly selected small subsets (each same size) of the training data set, I get different results in accuracy (the accuracy varies from 0.75 to 0.85). I determine the ...
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3answers
64 views

How do I handle with my Keras CNN overfitting

In my CNN, I have 700 images of class 0, 700 images of class 1, and 72 validation images. My code: ...
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0answers
18 views

Pytorch testing/validation accuracy over 100%

So I was training my CNN for some hours when it reached 99% accuracy (which was a little bit too good, I thought). But then it didn´t stop and it went higher than 100%. So I thought, that must be ...
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1answer
38 views

How does Neural Network denoise an image?

I understand the mathematical formalism behind how neural networks work as a classifier or perform regression analysis. But I face difficulty to realize how they are such a great denoising instrument. ...
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0answers
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List of CNN for Emotion/Sentiment recognition on images with performance on main datasets (IAPS, GAPED, EmoPics, NAPS)

There are more and more databases of pictures classified or rated with emotions. For instance, I know of 4 databases (IAPS, GAPED, EmoPics, NAPS) rating pictures on 2 dimensions: Valance (positive vs ...
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How to train the predicting boxes in a YOLO network?

I have just finished this tutorial that explains how YOLO networks work. Instead of training the network's weights with a training set, the author loads pre-trained weights and uses them to test the ...
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1answer
32 views

Getting Validation Accuracy of 99% with MNIST with less than 10000 parameters CNN

Given MNIST dataset in keras,the challenge is to develop a CNN neural net model with less than 10k parameters with 99% validation accuracy. I tried making the model for the same but am getting ...
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1answer
82 views

What I'm doing wrong with my CNN Keras?

In my project I have 700 images for each class (pdr and nonPdr) totalizing 1400 images. To validation I've put 28 samples. The problem is that my validation loss and accuracy is unstable. This is my ...
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0answers
5 views

What are different algorithms/methods of selecting triplet's for training a face recognition network?

I want to construct a siamese network using a triplet loss function. For which we need to select training samples( triplets ) for training the network, So how do we select hard triplet's for training ...
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1answer
53 views

Can we use Binary Cross Entropy for Multiclass Classification?

In this link, the author has implemented a CNN which classifies 15 classes and has used Binary Cross Entropy as the loss function. But since it's multiclass classification, is it valid to use Binary ...
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1answer
12 views

I'm not getting the no of parameters concept in CNN

Hi guys i've attached two images of question please help me on solution. Thank you
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1answer
37 views

CNNs: understanding feature visualization Channel Objectives (SOLVED)

I'm trying to follow a paper on deep NN feature visualization using beautiful examples from the GoogLeNet/Inception CNN. see: https://distill.pub/2017/feature-visualization/ The authors use ...
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0answers
20 views

Batch Normalization with CUDNN

I want to introduce Batch Normalization in my C++/CUDNN implementation of CNN. The implementation is currently performing well (without BN) on the MNIST dataset. I am using the CUDNN implementation ...
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0answers
29 views

How to use TimeDistributed fo CNN+LSTM?

I am trying to classify 6 classes time-frequency domain signal (STFT spectrogram) with a size of 3601x217 pixels. Assume that for each classes have 70 training samples, 20 validation samples, and 10 ...
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1answer
96 views

What's the principal difference between ANN,RNN,DNN and CNN?

I'm newer to deep learning domain. I would like to know what is the principal difference between RNN,ANN,DNN and CNN? How to implement those neural networks using the TensorFlow library?