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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How to implement CNN crop area as learnable parameter?
I'm currently implementing a 1D CNN to forecast a time series for an industrial process. Essentially, I give the model 30 time steps (1 time step = 1 minute) of input data captured from 7 different ...
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Image classification approach for float outputs
I have an image input and the model should be able to predict its 15 feature values as output. I am being told that i should use an image classification model to solve this. can somebody suggest me a ...
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optimization of multi-regression CNN architecture
I use T4 GPU on Google Colab to train a model for multi regression. I use to training 30k RGB images 256x256, 5k to validate, 7k to evaluate. The model has 11 outputs [0-1] range (the sum of outputs <...
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Traditional 2D CNN Formula
In [4D U-Nets for Multi-Temporal Remote Sensing Data Classification] they give the following formula for the traditional 2D CNN. But I’m confused about the w_i,j in this formula:
From my knowledge I ...
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YOLO v5 labelling dilemma
let's say I want to detect 3 different types of objects in an image using transfer learning from YOLO v5.
I have only 1 custom input image, with over 2000 labels comprising of all these 3 unique ...
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Stream in images to keras from disk to avoid blowing up the memory
I have the following model that is supposed to zoom out (outpaint) images. I have a dataset of 20000 300x400 images. The problem is that neither my GPU nor RAM can hold all images at once. So this ...
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How to copy and crop feature map in Unet?
I am confused about the principle of copy and crop in U-net, like the grey line shown above.
For example, the first grey line, how to convert a (64, 568, 568)(C,W,H) to a (128, 392, 392), did the ...
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How to apply convolutional & pooling operation on an image
I am learning convolutional & pooling operation. I want to use them on the following image using tensorflow. This is an image of width=600, height=400 and 4 dimensional color channels.
I want to ...
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Batch normalization getting Val accuracy of 000e00
I am currently stuck on batch normalization. I have code written out but when I do it it keeps giving me a val_accuracy of 00e00 and stops after 2 epochs (I am wanting to run 20). I am not sure what ...
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Test accuracy different results per training despite fixed randon seed
I'm training a tensorflow model which classifies hyperspectral images.
The randon seed variable is set.
On one system I am getting the same test accuracy every time I train it (training is done on GPU)...
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Overfitting still exists using different techniques on voice classification
I have 986 voice signals which have been collected by our team. The data set includes 745 healthy and 150 unhealthy voice signals. I split the data into 70% training and 20% validation and 10% test (...
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Why not decrease the number of filters in each 1-dimensional convolutional layer for spectrogram processing?
In convolutional neural networks, the number of filters usually increases with every convolutional layer. Why is this common practice?
Nonetheless, these networks typically process images. When ...
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Rotation invariant template matching using CNN or openCV??
I have 4000 unique images of sample data from intersections and the start and end point from all lanes (approach and exit lanes) on it.
I want to train a neural network on these images to find the ...
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Training and Validation accuracy decreases after using more data
I have a binary classification project, I use a neural network with the following architecture:
The shape of the input is 64×64×4. This input was fed to a Conv2D layer with 32(5×5) filters followed by ...
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Why does this cGAN model perform poorly when trained on a different machine?
I was sent a cGAN model python file from a friend + the dataset he used to train this model. For him, the model trained succesfully & was able to generate very accurate images. These were his ...
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ReLu layer in CNN (RGB Image)
I am able to get convoluted values from RGB Image lets say for each channel.
So I have red channel with values: -100,8,96,1056,-632,2,3....
Now what I do is that I ...
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Pytorch mat1 and mat2 shapes cannot be multiplied
The error message shows
RuntimeError: mat1 and mat2 shapes cannot be multiplied (32x32768 and 512x256)
I have built the following model:
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why by adding additional information as number of sequence on dataset can avoid overfitting
I am developing a regression model to analyze walking styles. The dataset I am using to build the model is from 2 different sources, let's call them dataset A and dataset B. Dataset A has a shape of (...
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Why don't we increase the parameter from 64 to 128 in this CNN model?
I'm looking at an example lab from a coursera course titled Intro to Tensorflow. In this CNN model, they're gradually increasing the no. of filters from 16 to 32 and then 64. Why don't we increase it ...
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why CNN the model can't predict 0
I have two datasets: force plate data and plantar pressure data. The force plate data consists of 6 data points, while the plantar pressure data consists of 90 data points. Both datasets have a ...
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Is deep learning high initial validation accuracy a sign of problem?
I have a image classification model with 8400 images of class A and 1800 images of class B. I have used validation_split=0.2 with subsets of ...
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Object Detection: Setting threshold values as trainable parameters?
I am building my first object detection model (Mobilenet SSD, to detect animals in images) and happy with the current test results.
When I tested it using images without bounding boxes, I noticed some ...
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Determining "filters" dimension after a convolution operation
I tried to calculate the "filtered" dimension and I seem to be getting it wrong.
Below there is the image I am trying to calculate the "filtered" dimension for, where you have 192 ...
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CNN model well trained but can't predict real data
I'm developing a CNN regression model for gait analysis. It seems the model is well trained, with low val_loss and low loss. However, the model does not work well to predict real data. In this ...
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why MSE will be high when I inverse data
I make a regression model to predict force plate using plantar pressure. I am trying to use CNN model in this case. I have 2 different datasets, dataset A (force plate data) and dataset B (plantar ...
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How to visualise a feature map or filter in a group equivariant convolutional neural network
I'm reading an article called "Group Equivariant Convolutional Networks" by T. Cohen and M. Welling (https://arxiv.org/abs/1602.07576) and I'm having some problems understanding one of their ...
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How to build an image generation model for interior room design?
I want to build an image generator model of interior room design. This model should be able to generate an interior image of a living room/bedroom/hall/kitchen/bathroom. I have searched about it and ...
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Which CNN model to use for the classification(20 classes) of gemstones (diamonds, sapphire, ruby etc) based on digital photo images and huge data set?
Im trying to build CNN Model for the classification of precious stones (like diamonds, sapphire, ruby) based on digital images. So I have data set of labeled 150,000 gemstone certifications and the ...
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Grad-CAM for CNNs with GAP layer
I'm new to deep learning, so maybe this is a silly question...
Do any adjustments need to be made for applying Grad-CAM on CNNs that use a Global Average Pooling (GAP) layer right before fully ...
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Are there any advantages on considering images as graphs and use them on Graph Convolutional Networks?
I have seen this encoding of an image as a graph:
The set of the nodes $V$ is the set of pixels. If the image is of size $10\times10$, then we have $10\cdot10=100$ pixels.
Each node has a length 3 ...
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Would a CNN be an appropriate model for a board game?
I want to make a reinforcement learning algorithm for a game. It's a turn-based game that takes place on a board. You would basically have 4 typical actions: attack and rush the enemy, attack and flee,...
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What are some methods to convert time series data into images for CNN?
I am working on a project where I have specific time series data which I would like to convert to images. I have investigated various methods, such as Markov Transition Fields, Gramian Angular Fields, ...
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Grad cam outputs for all the images are the same
I am using grad cam to see which regions of the test images are most important for the prediction of resnet50. The output I got has some errors.
Code Snippets:
<...
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Can early stopping be used with stratified k-fold validation to help avoid overfitting in neural networks?
I am using stratified cross validation and using transfer learning for the classification. I have 4 classes. I am training my model with fold=10 and epoch=20.
code snippets:
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Regularization in CNN change training accuracy but not validation accuracy
I have a question regarding regularization in convolutional neural networks.
So I'm building a CNN for image classification and I've come across something I don't understand.
Without using any ...
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Validation acc is very high in each fold but Test acc is very low
I am trying to implement a neural network. I am using CNN model for classifying. First I split the dataset into train and test.
Code Snippet:
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Shaping Data for time series ConvLSTM
I am having the same problem and I am unable to properly fit the input to the model. Can u please share some of your code snippets. mainly for input data reshaping and passing into the ConvLSTM2d. ...
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Manual computation of the predictions in a convolutional neural network
I am trying to manually compute the predictions of the Keras library for a convolutional neural network. However, I am struggling a lot to match my final result with the ones provided by Keras. I do ...
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Measure distance between teeth using Machine Learning
I'm a newbie in ML and I have a problem I am stuck on.
I want to train a ML model to recognize dental diagnosis based on photos and x-rays of the patient. Specifically right now, I want to find a way ...
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A curve val_loss and loss in keras after training a model
Can anyone help me, is my model overfitting or underfitting?
I want to make sure the model is well done before starting to deploy
Also, I use categorical cross-entropy loss
I have asked before, but I ...
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Curve val_loss and loss in keras after training a model
I trained a Keras model to diagnose disorders and want to make sure it is good enough to start deploying.
From the below graph, can anyone advise me as to whether my model is overfitting or ...
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In CNN, When do we increase, or decrease, the number of filters/neurons?
Good morning,
I would like to understand how do we choose between increasing or decreasing the number of filters applied in a CNN.
My logic response to this, would be to take Autoencoder as an example ...
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WARNING:tensorflow:Your input ran out of data; interrupting training Error
Due to VRAM capacity limitations, I cannot fit the whole training and validation data into the GPU memory. Instead of cutting some of the data out, I decided to use TF.dataset object to create the ...
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Validation accuracy decreases when I use ImageDataGenerator instead of manually creating labels
I was getting 85 percent validation accuracy before using ImageDataGenerator, now validation accuracy for binary classification decreases even to 14 percent. and never passes 60 percent. I couldn't ...
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problem on designing a custom_loss function
I am using CNN to solve a regression problem in a supervised manner. i have input data(X_train) and the target data(y_train). I allow the network to train and during training process in each batch of ...
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Adding Data sequences as unique data on dataset for regression model
I want to predict a force plate using plantar pressure. The shape of the force plate data is a 15000x6 array, and the shape of the plantar pressure data is a 15000x89 array. I will use a regression ...
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Binary Classification: My model classfies most data (95%+) as label 1
I am working with ECGs and trying to use a CNN model to perform binary classification. The goal is to classify 30s ECGs to detect a specific disease. I am using CNN and converting ECGs to images (...
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Should a CNN generalize to arbitrary positions in the data?
I have trained a CNN on one dimensional data that is the power spectral density (PSD) of a $N$ different classes of signals ($N=4$). Each of the $N$ signals has a different spectral shape (not shown ...
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Using features extracted from CNN and handcrafted features to perform classification
I have a question in regards to merging features extracted from CNN and handcrafted features.
I have been reading this paper https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002895/#B33-sensors-22-02467
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Best Tool for NN with Convolutional Layers
I am working on creating a NN with the following architecture:
Input layer (180 neurons)
Hidden Layer 1 (18 neurons)
Hidden Layer 2 (4 neurons)
Output Layer (1 neuron)
I am trying to figure out the ...
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