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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CNN model not converging

As said in the title, I made a CNN model that is not converging. The purpose of this model is to take a spectogram as input and produce a phoneme(https://en.wikipedia.org/wiki/Phoneme) output (audio ...
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Preprocessing of 3D CAD files for Keras Conv3D input

I'd like to apply some machine learning on 3D CAD data. File format should ideally be mesh-based like STL. Keras offers 3D convolutional layers (https://keras.io/layers/convolutional/), so it can ...
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Detection of UI Elements using CV

I have a task to detect UI elements in the photo of the touchscreen. I did several decisions based on the research that I did, but I am no very confident with them so I would appreciate feedback on my ...
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i can't understand whether there is problem in code or anything else.can somebody help me with this problem [on hold]

My model does not seem to converge to converge especially on the validation set. Here is my code : ...
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18 views

How can I regularize the output of a layer from scratch (without using Keras)?

I am trying to build a Convolutional Neural Network after reading notes from Stanford's cs231n course. I use ELU activation as activation function, and SoftMax as my classifier. Architecture is simple:...
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Do I need different CNN architectures to detect the same objects for the same dataset with higher fps and higher resolution?

I am planning to do object detection on a dataset that is 5 fps with a resolution of 720 x 320. After training that CNN on that dataset, how significantly should I modify the CNN architecture to ...
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Weka Character Level CNN

I am wanting to use a character level CNN to classify a heap of documents based on the century which they are from. I am having difficulties finding any resources on how to do character level ...
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Different convolutions in CNN

I have a simple question. Why only convolution is used in CNN? There are a lot of possible rules for combining a filter and an image. Why is pixel-wise convolution the standard? For example, dropout ...
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1answer
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Can the same CNN architecture be used for different data sets?

I have a CNN architecture that works well on 32x32x3 images. Can I use that same architecture for a data set made up of 28x28x1 images? (Both data sets have 10 classes). If this is possible, what ...
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24 views

Semantic Segmentation using Keras: loss function and mask

I am about to start a project on semantic segmentation with a grayscale mask. The thing is, we have to detect for each pixel of the image if its an object or the background (binary class problem). I ...
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Mixing unsupervised and supervised algorithms in image classification model

I am trying to replicate the general image classification model used in a paper that I cite later below. The following image is an extract from a paper that proposes a novel method of performing image ...
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Kera CNN TypeError: float() argument must be a string or a number, not 'NoneType' [migrated]

I tried to set up a binary CNN with Keras using ImageDataGenerator and flow(). I have looked at other threads with similar ...
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CNN implementation low accuracy on MINST data

I'm trying to implement VGG11 (Model A of Table 1 from https://arxiv.org/pdf/1409.1556.pdf) on the MINST dataset but I'm getting ~10% train & test accuracy (as bad as random guessing). I had to ...
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Accuracy of CNN on images taken under different conditions

I have a dataset containing images taken under 4 different conditions. When training the model, I use the same proportion of images (25%) from each condition. Then, I'm testing on 4 different test ...
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Res Net Reduction Block

I am building a ResNet. I have two separate blocks: Cnn block, Reduce block. Cnn block - 1 cnn layer, activation, Batch Normal -> 1 cnn, activation, Batch Normal, so 2 CNN in this block. In Reduce ...
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How should I retrain the CNN for text extraction

I am working on a text extraction problem from Invoices. I want to detect various fields in the invoice like the following. I am struggling to find any dataset for invoices. I have a dataset of 150 ...
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CNN models comparison

I coded a 38 layer CNN and 8 layer CNN but there's something wrong in my 38 layer CNN, which doesn't learn anything. Not able to fugure out what's wrong. They were trained on CIFAR.
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How do I run SMOTE on image data using the packages available?

I need to balance some image datasets, how do I use SMOTE variants or the imblearn SMOTE method with images? I can't figure out how to, since they seem to be working only with numerical datasets.
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What model should I use for multiple time series input

I want to predict bacteria plate count from time series(around 10000 values in a row) of water temperature in the water on a one minute granularity, and other daily climate data including min and max ...
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The model only improves Precision/Recall AUC

I have a CNN model for an imbalanced image classification problem. I'm experimenting with a theory that is supposed to improve the accuracy of the model. Since I'm dealing with imbalanced data, I'm ...
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Should I remove the background of my training images?

The images in my dataset look are as below: The images have either a purple background or a white one. But the trained model (cnn) will be tested on images from the field ,that is, they will most ...
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Convolutional Neural Network for Structured Data

I am having a student dataset which is a record of student academic details I know that that CNN is mostly used in computer vision and image processing for analyzing visual imagery But here it is ...
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1answer
26 views

How reproducible should CNN models be? [closed]

I want to train several CNN architectures with Google Colab (GPU), Keras and Tensorflow. Since the trained models are not reproducible due to GPU support, I would like to train the models several ...
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Why does my model sometimes not learn well from same data?

I have a dataset of 2 classes, both containing 2K images. I have split that into 1500 images for training and 500 images for validation. This is a simple structure for testing purposes, and each ...
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Convolution with different dilation rates for each dimension

How can I get a dilated convolution with different dilation ratio on each axis? Tensorflow/Keras would be best. For example, the filter in the gif below would have the following properties: ...
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16 views

CNN model with transfer learning not performing, training loss is still high, test accuracy is very low

Hi I'm trying to train a cnn model with transfer learning, and I am not able to get a good test accuracy (14%) - I don't know why it doesn't work for me. ...
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1answer
28 views

Issue with output dimensions in keras

I'm currently trying to build and train a model for CIFAR data using keras. My labels should be one-hot encoded. data.y_train.shape is (45000, 10). My model is ...
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2answers
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Bib number recognition using Keras

I want to implement a racing bib number recognition application (for study purposes) using Keras API. I can manage training a CNN model to detect different numbers but how to localize only specific ...
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1answer
32 views

Deep Learning for non-continuous dataset

I am working with this dataset which is record of student academic details and I want to predict the student's performance. since the dataset is non-continuous I cannot apply CNN on this dataset. ...
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How to find which patch in orignal image does an activation correspond to in vgg net after the final pooling layer

So I am working on the NeurIPS 2019 reproducibility challenge, The link to the paper is https://arxiv.org/abs/1806.10574. So basically we have a vgg-16 net with the final fully-connected layers ...
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1answer
58 views

Why does adding random pixels stop my model learning in cnn?

I am using a very simple model to classify a 224x224 RGB image. For a test, I have labelled my images (2 labels "Green" or "Red", 2,000 images of each) based on colour of a single fixed pixel from ...
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Machine learning algorithm for classifying a 2xN array of ranged coordinates?

Good afternoon, I have a dataset of lists of coordinates that are ranged from (0, 100) on the Y-axis and (0, 300) on the x-axis, with double precision. I'm looking into classifier algorithms that ...
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1answer
56 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
28 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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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
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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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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
27 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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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
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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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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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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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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
31 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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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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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 ...