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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Should I resize my object localization dataset in order to train a fully convolutional network?

I want to create a fully-convolutional neural net that trains on wider face datasets in order to draw bounding box around faces. The dataset is highly diverse in the image sizes. So my question is, ...
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Representing the architecture of a deep CNN [duplicate]

Suppose I am feeding a $60\times60$ RGB image as the input to deep CNN with the first layer created using the following Keras code ...
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Get data from intermediate layers in a Pytorch model

I was trying to implement SRGAN in PyTorch and I have to write a Content loss function that required me to fetch activations from intermediate layers for both the Generated Image & Original Image. ...
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For SHAP (SHapley Additive exPlanations) which one is the positive and negative class?

My CNN model has to classify data into 3 classes say A, B & C which is one hot encoded. ...
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Should kernel size always be a prime number?

Should kernel size always be a prime number? E.g. (3,3) (5,5) (7,7). While tinkering with sklearn.preprocessing.KernelCenterer(), I noticed that I could only get it ...
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mask rcnn background removal in video [closed]

I have been trying to implement mask rcnn human detection in videos. Are there any codes available for this purpose? Also, background removal is required. https://www.youtube.com/watch?v=FCz4Ai1TtEM ...
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feature importance with CNN

I can use shap to extract important features for Dense NN. However, for CNN, I encountered two problems: the feature order may be messed up or combined after the ...
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Understanding the layers in CNN

I am new to NNs and I have a question about the convolutional layers in CNN. A convolution layer is said to perform feature extraction or work as a feature extractor in CNN. The first convolutional ...
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Number of parameters (weights and bias) in yolo

How many weights and bias YOLOv1 have in total?
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CNN inference is slow on Jetson Nano

I'm running what I believe is a pretty lightweight CNN on an nVidia Jetson Nano with Jetpack 4.4. nVidia claims the Nano can run a ResNet-50 at 36fps, so I expected my much smaller network to run at ...
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How to address overlap in categories for image classification?

I am trying to build an image classification model using CNN for classifying food products on the basis of allergens that they contain (i.e. on the basis of food being lactose rich, histamine rich, ...
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Theoretically Speaking, How Do Squeeze-and-Excitation Blocks Help?

A SE block works by assigning a weight to each channel, contrary to a vanilla filter, which gives equal importance to all channels. My question is, theoretically speaking, shouldn't a regular filter ...
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How gradients are flown back to Network in siamese architecture? How weights of all CNN models are same even when using different models

TL;DR : Intuition behind the gradient flow in Siamese Network? How can 3 models share same weights? And if 1 model is used, hpw Gradients are updated from 3 different paths? I am trying to build to a ...
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Auto encoders to convert black and white image? [closed]

I am trying to get an intuitive understanding of auto encoders and how it works colorizing B/W images my questions are, What kind of input data and test data is needed? What will be the class labels? ...
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Help Interpreting Machine Learning Results [closed]

I am trying to implement a machine learning model for a regression task in bioinformatics. It is a task analogous to scoring images. The goal is to get the predicted label for an input as close to its ...
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Concatenation of CNN and LSTM to model time of a series of images

I have collected a dataset consisting of around 30'000 heat maps of 80 users. The heat maps represent typing behavior on a keyboard and are just images with a resolution of ...
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Differential Learning Rates To Train Parts of A Network Faster

So I've had a rather "out there" idea. I want to train a dense network on a regression problem based on tabular data but I'd also like it to incorporate image data. My idea was to use a CNN ...
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What are some general tips to improve my MNIST classifier?

I have built a CNN from scratch in python using Numpy, to tackle the MNIST hand-written digit recognition problem. It's composed out of a convolutional layer (3 3x3 filters), a maxpooling layer (2x2 ...
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One hot encoding of target variable containing classes 1 to 9 not including zero

While predicting a solution for a sudoku puzzle using CNN, the target variable should predict values from 1 to 9 for all the 81(9*9) values in the puzzle. Hence the target value shape is (81,9). Using ...
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What should be the best CNN model for Feature extraction of images for Image-Image search engine using LSH?

I have images something like the below: And like this: I have a huge data around 20M or so am I want to apply de-duplication and improve the search for this ...
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How to fit pretrained yolov4 model to other class?

I have already trained model for one class, so I have weights and how can i train it for second class? This model detects cars 100% and I need it to detect vehicle plate number, I used yolov4, darknet ...
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Toeplitz matrix in convolution neural network problem

Instead of multiplying the kernel with input vector iteratively, the convolution operation could be written as matrix multiplication. Infact this is how convolution operation is implemented ...
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Pretrained CNN model on animal dataset ( turtles images if exist )

I was wondering if there is a pretrained CNN model on an animal dataset. I am participating in a turtle face detection competition and was wondering if there was a pretrained image model to fine tune ...
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Why decreasing the number of convolutional layers inside a CNN increases the number of parameters?

I am building a CNN from scratch, and I am trying to change the number of convolutional layers to see what happens. I have noticed that decreasing the number of convolutional layers increases the ...
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Multiple input single output supervised learning ANN problem

I have a dataset of 120 tuples giving a singular output. I want to implement ANN in estimating the input which is affecting the output most. A case of optimising the input to maximise the output. ANN ...
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How to train YOLOV4?

I am going to write yolov4 real-time object detection, and I have to do it for car then vehicle plate number, but it does not have to find plate number if there is no car, first car then number on car,...
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Wavenet - how are the skip connections from the residual blocks utilized?

I've been attempting to implement the Wavenet paper: https://arxiv.org/pdf/1609.03499v2.pdf In the paper, the main diagram they use to describe the architecture is this one: The paper mentions the ...
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Transfer learning from great labelled time series data to one with low quality labelling

I have a source dataset containing outputs from a sensor per minute and have made extra effort to label them correctly for approx. 3 weeks. I trained CNN-BLSTM network on that dataset which classifies ...
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Oscilations in loss curve [closed]

I saw a similar question, but I think my problem is something different. While training, the training loss and the validation loss move around one number, not decreasing significantly. I have 122707 ...
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Adapting ZFNet on 2244x224 image using a filter 7X7

I am building a model based on ZFNet in Tensorflow 2.0. I am using the Petal images dataset. The images are of size 224x244x3. So my question is when implementing the first layer (conv2d) with filter ...
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Keras inverse of LocallyConnected2D

In Keras, the inverse of Conv2D is Conv2DTranspose. But how can I create the inverse of LocallyConnected2D? That is, using "local" connections, how can I map a small set of nodes into a ...
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How do I deal with the fact that I have images which are not consistent with the class they belong in an image classification problem with CNN?

I am really new to Neural Networks and to Machine Learning in general, and I have been given a dataset composed by images for performing multi-class image classification with a CNN. The images were ...
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How to define the input channel of a CNN model in Pytorch?

In pytorch, we use: nn.conv2d(input_channel, output_channel, kernel_size) in order to define the convolutional layers. I understand that if the input is an image ...
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How do I interpret loss and accuracy per epoch while training a CNN?

I am really new to Neural Networks, and I am training a CNN for image classification, and while training, I get the following: which tells me the training loss and accuracy and validation loss and ...
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What do the parameters used in crop mean?

When we have an image to be used as an input to a CNN and we want to classify only part of the image, we usually feed the classifier with a crop of the image. Lets say my image is called frame and <...
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How to solve ValueError: Negative dimension size caused by subtracting 3 from 1 for '{{node model/Conv1/Conv2D}} = Conv2D… in mobilenet_v2

I'm trying to apply a retrained model of mobilenet_v2 presented in https://github.com/balajisrinivas/Face-Mask-Detection The ...
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Strange behavior of CNN when forecasting time series

I have a time series containing 5 features. I tried to use LSTM to predict the next 112 periods in the series. However, I got very bad results. So I tried to use CNN. First, it did not work properly ...
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Keras ValueError: Shapes (64,) and (32,) are incompatible

I am trying to run my first CNN model on the Fashion MNIST dataset. I am using kerastuner to tune the hyperparameters. The below code gave me an accuracy of 90.4% on test, 92.2% on validation and 94....
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Why is everybody using `mobilenetv2` for mask detection?

I was looking for good pre-trained models to be used for mask detection and I found resnet50 and mobilenetv2 (lots of times). ...
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1answer
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How to build a classification pipeline that will pass to another model?

Not sure if the title explained it, but I am trying to build a pipeline where it's like a decision tree, but also not. Say for example, I had a picture. The model classified the picture, but now I ...
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I have no access to gpu due to usage limits?

I start running my code using google colab I first set the execution to GPU and then I run my code for a training task using keras !after 1 hour I got a message saying I can't use GPU due to usage ...
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How to deal with a binary classification problem, where the instances in the negative class are very similar? [duplicate]

Let's say, one wants to detect, whether a picture of a fixed size contains a cat or not. But as a dataset, you have 10000 pictures of cats, and 30000 pictures which don't contain a cat, but are very ...
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Reduce overfitting in a CNN model

We are Data science students and we are building a CNN model to pneumonia classification (dataset: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia ). We have applied a data augmentation ...
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Why does GPU speed up inference?

I understand that GPU can speed up training for each batch multiple data records can be fed to the network which can be parallelized for computation. However, for inference, typically, each time the ...
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How to store efficiently very large sparse 3D matrices

To train a CNN, I have stacked arrays of images over observations [observations x width x length]. The dataset is very sparse ($95\%$). What would be an efficient ...
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Dying leaky ReLU

I am trying to train a deep neural network but I am having dying ReLU problem. I am using leaky Relu but still have the same problem. Isn't leaky relu supposed to not have such problems?
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improving performance for a limited dataset with noisy images, pattern recognition

I am trying to recognize doodles in noisy images like in this one below. My dataset consists of only 10 000 images and 30 categories I've implemented a CNN but it is giving me a 6% accuracy. I am ...
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How can I fixed the filter and Kernel Size of a CNN?

I have created 4 x 4 2d images from a signal. Now, I want to feed this data to a Convolutional neural Network. How I can choose the nubmber of ...
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Sneakers representation learning

I am trying to make a model which would take an image of shoes as an input and output a meaningful N-dimensional embedding of the shoes, so that they could be searchable/comparable/clustered and used ...
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How are pictures pre processed before being used as ML data

So I was watching this YouTube video So basically the professor used ML to generate random faces in order to create data for a Kaggle challenge. When I looked into the data file, I was expecting to ...

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