Questions tagged [convolutional-neural-network]

A convolutional neural network is a form of neural network with an additional convolutional layer, typically used in image & audio analysis. The convolutional layer is essentially a filtering stage defined by the kernel which is used. For example, a convolutional layer could have a kernel which extracts edges from an image towards the goal of learning which objects are in a scene.

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More dense layers with heavy dropouts or fewer layers with light dropouts?

I'm trying to build a network. While creating the fully connected part in the last, Which one should we prefer: More layers that regularly reduce with heavy dropouts or fewer layers that reduce ...
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Machine learning algorithms for tabular dataset

I have a dataset with 120 features and 5000 instances. The dataset is combination of categorical and numerical values. It is a tabular dataset. My problem is a binary classification problem. I trained ...
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Which loss function to use for a convolution NN for noise removal of high resolution images

My task is to remove small random spots from my 4 mega pixel images. My strategy was to feed a convolution network these images as I have the true images without the spots in them. The current loss ...
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Python pretrained model vgg19 predict all images in a directory

I am using VGG19 for transfer learning (I have 9 classes in my new model) and I want to use the build-in decode_predictions method to output the predictions of my model. However, I have an error when ...
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Testing and validation with flux for a large dataset

I'm using Julia. I've a dataset for train and test. I am not sure about validation and testing. So the loss function (which is the objective function which updates parameters) it only reports loss on ...
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How to stop my CNN getting confused between 3s and 8s, and 1s and 7s?

I am trying to train a CNN, using the MNIST dataset (which I perform data augmentation on), to classify numbers on a sudoku grid from 0-9. While mostly successful, my network seems to get confused ...
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How to optimize transposed convolution?

I have implemented an encoder-decoder architecture-based neural network with Neural Network API(NNAPI) Android Ndk. There are 5 encoders and 5 decoders. The first encoder's input dimension is -> ...
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Training and validation accuracy extremely low for Autonomous Lane Navigation via Deep Learning

Using a SmartCar running on RPI4 i collected all the images necessary for training. Training is done using CNN Nvidia's Model with Tensorflow and Python. Took about 900 Images for Up and 800sh for the ...
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Is backpropagation applied every layer the same?

For example, I have layers that are pretrained. But while predicted, the loss is very high. But not because of pre-trained layers. Because of not pretrained layers. Will every layer be affected by ...
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Recognize chatbox on game screenshots

I have videos from a computer game. In this computer game, during the rounds, there is a chat box where players can write messages. I want to read the content of this chatbox. Difficulties are here: ...
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What is the effect Cross-Multi-Labeling/Annotation on learning process?

I have a philosophical question regarding training convolution neuronal network. I am work on training NN for purpose of detection of Window and Window blind. This is an issue of cross labels; that is,...
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What do filter numbers signify in a convolutional neural network?

I'm new to deep learning and currently trying to figure out the basic concepts about convolutional neural networks. I understand how such a neural network processes images, but there is one thing that ...
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Increase and decrease in depth of CNN blocks

Please see the image below as an example. To my understanding, the 1nn image is converted to 500*n~ * n~ with the help of 500 kernels applied on the same image. What I am confused about is how in the ...
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Using the results of clustering to retrain a neural network

I am following and expanding upon previous work from the winner of the Melanoma Classification from here. The dataset has 9 classes. The competition is only interested in the one class (Melanoma). I ...
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ML/DL model needed to perform binary classification on binary input image dataset

I desperately need help regarding ML/NN models that would be appropriate for binary input data.. So, I have an image dataset in which [R,G,B] values can only take ...
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post img labeling transformations

I am using labelImg.py (https://github.com/heartexlabs/labelImg) to label my training data set for a CNN I have (YOLOv4). in order to save on time, I would like to label all 400(ish) images, then ...
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Format 2d numpy arrays for 2D CNN

I have a dataframe where each row contains the 2d numpy array of an image (200x200) like: Given this data format, how do I convert this data into a form where I can use a TensorFlow CNN model on it? ...
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tensorflow is not learning

I am writing this code from the tensorflow tutorial about Autoencoders ...
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How does a CNN work in detecting absence of features/objects?

I'm trying to understand how a CNN operates internally. Let's say I'm doing binary classification with 1 output neuron and a sigmoid to classify dog vs no dog. No dog meaning the image does not ...
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What does the no of nodes represent in a Convolution Layer?

What does the number 16 (no. of outputs) represent in this Convo layer? layers.Conv2D(16, 3, padding='same', activation='relu'),
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How to explain the high accuracy and F1 score on the test set with a huge binary crossentropy loss?

I'll provide a little of introduction based on my example. I have a small collection of RGB (but 'gray-looking') brain MRI photos, divided into 2 classes: healthy and tumor. My data split looks like ...
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Why is my DCGAN not converging?

I'm training a tf DCGAN on the MVTec hazelnut dataset and I found some difficulties. The problem is that after a lot of epochs the generate does not produce some quality images. My model is the ...
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I want to extract only the red and blue lines or enhance these lines in the image provided for easier detection by my CNN model

I am trying to check if the "photo" in the image(some ID card) is forged or not, for that I have done some ELA transformation(found from Kaggle) on the original image which provide me with ...
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Mask R-CNN (matterport) does not generate masks or just generates them randomly

I'm working on a project detecting two different types of olive branches. I'm following this code (based on matterports Mask R-CNN) with my own dataset: https://github.com/AarohiSingla/Mask-RCNN-on-...
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Classifying Hot Drinks

I have a university project in which I will attempt to build a machine learning algorithm to classify images of hot drinks (e.g tea, coffee, etc) and I was wondering about the best approach to do this....
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Bending Training Loss, what could be the cause?

Hello, during training of one of my models, I observe the following training (blue) and test (orange) loss patterns. At first, the training loss increases, then bends and starts decreasing. Just ...
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GNN Model - Analyzing Training Curve

Introduction. Actually, I am working on a Graph Neural Network (GNN) model to predict some graph-level float values. So, input=graph, output=float predicted value. I trained and evaluated the proposed ...
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Siamese Network constant accuracy with more training data

I am new to machine learning and I am currently trying to create a siamese network that can predict the similarity of brand logos. I have a dataset with ~210.000 brand logos. The CNN for the siamese ...
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Fine tuning Convolutional Neural Network with a learnable first layer

I have a classification task using grayscale images and I want to leverage from pretrained networks. There are a lot of resources out there presenting how to fine tune large neural nets like resnet, ...
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1 answer
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Found input variables with inconsistent numbers of samples: [908, 9080]

I have a dataset, I have reconfigured my tensors as a single 3072 sized line array. I have reconfigured the valid dataset and training dataset. You can find all of the information about my train, ...
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Different Kernel Initializers in my prediction layer with Transfer Learning could affect performance?

So I have this model right here and the task is to classify 3 labels.: ...
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Pytorch CNN in_channels, out_channels for Classifying DNA Sequences

Apologies as this question has been asked before -- I'm really trying to wrap my head around the motivation behind designing neural network architecture. I'm designing a convolutional neural network ...
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Implementing the backward step for conv2d layers

I am trying to recrate the conv2d layers using the eigen library but I have some problem understanding how the backward step for conv2d layers is calculated exactly. Before I go into explaining my ...
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Using CNN to extract channel network from old maps

I am a numerical modeller working on a flow problem. I have developed a channel network simulator to model fluid flow through an irrigation network. As part of my inputs I have to use old maps (as old ...
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Which representation of CNN feature maps is correct?

When I extract my features from my CNN, it doesn't look like this: And those pictures are not just representation. From this article it can be seen that these features are actual extracted features ...
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Style Transfer - how to choose the cnn layers?

I read this tutorial. In that tutorial they choosed: conv layer #4 for content_layers conv layers: 1, 2, 3, 4, 5 for ...
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What happens in a transposed convolution when the stride is bigger than the kernel width/height?

In Pytorch's transposed convolution API, you can specify a stride that is larger than the kernel_size. For example: Input image of size 2x2 Kernel of size 2x2 ...
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Dealing with variable length videos where frames are multi class in Keras' temporal convolutional networks (TCN)

I am working on an action segmentation problem whereby I have multiple videos of different lengths containing several actions. I have created features for each video which are also of variable length. ...
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How to see Latency at layer granularity in a CNN

I am finding documents or an example that measure Latency at layer granularity in the AlexNet model. Please could share or tutorial for me.
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How to add linear metadata to a 2D convolutional layer

I have an autoencoder which takes a 2D image as input and outputs a 2D image. The architecture is currently a set of 2D convolutions and RelU layers. I would like to add a ~10 element linear input ...
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How do I train a CNN using my own dataset?

I'm trying to run an image classification CNN on my own set of labeled images. Currently, I have the images of each label stored in folders, named by the labels. I can also store the images as numpy ...
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Transpose Convolution feature extraction

Convolution extracts high-level features, but what about Transpose Convolution (or De/Up-Convolution)? Does it behave exactly the opposite? Does it generate lower-level features?
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CNNs and classifying images in different scales

Let's suppose we want to classify images of the temperature distribution on a metal sheet using CNNs, but the dataset available has images in many different linear scales. I'm using temperature as an ...
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How can I fine tune a model to detect digits, used to detect denominations of currency notes

So the task at hand is to detect the denomination of any currency banknote. The dataset I have is about 2k images of each denomination (12 in total). An example banknote (after noise removal, erosion ,...
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How YOLO detects the object when the object is in multiple grid cells?

I have been reading various articles and watching videos on YouTube, but i cant seem to understand how does YOLO makes a bounding box for an object if it is in multiple grid cells? for example in the ...
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What can we learn from visualizing Feature Maps

I have the following classification model (dogs vs cats): ...
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How to add the Luong Attention Mechanism into CNN?

As I write my CNN model for an image binary classification below, I'm trying to add an attention layer to this model. I read from tf.keras.layers.Attention: https://...
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Why convolutional layer learns only biases?

I`m training a siamese CNN to distinguish between pairs of images and though my train/val binary cross-entropy loss values show negative trend, implying some of the model parameters are being updated, ...
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How to determine the number of Neurons in each hidden layer and number of hidden layers for face recognition [duplicate]

I plan to build a CNN for face recognition using this Kaggle dataset. I tried building a model with a single hidden layer with 256 fully connected neurons, and it gave an accuracy of 45% after 55 ...
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how does convolutional layer work? [duplicate]

I have one question regarding CNNs. If we take a single convolutional layer it can have multiple filters right? Are these filters all the same? Is a single layer made only to detect one feature? I am ...
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