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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weight decay in ResNet50

Can someone please guide for implementing weight decay in transfer learning approach? I want to regularize the pre-trained model ResNet50, where I'm fine-tuning the model for an image classification ...
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model has not yet been built

I'm making CNN-LSTM model for forecasting but I'm receiving this error : This model has not yet been built. Build the model first by calling build() or calling fit() with some data. Or specify ...
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How to reduce overfitting in a pre-trained network

I have a custom dataset with 10 classes and I am using a pre-trained resnet18 model from torch-vision. I can clearly see it's over-fitting because: the model is trained for 75 epochs with a batch size ...
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CNN gradients with different magnitude

I have a CNN architecture with two cross entropy losses $\mathcal{L}_1$ and $\mathcal{L}_2$ summed in the total loss $\mathcal{L} = \mathcal{L}_1 + \mathcal{L}_2$. The task I want to solve is ...
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Training set of a NN that classify Cat or Not Cat

Is it possible to build a Convolutional Neural Network (using Keras, Tensorflow) that can give output as 1 for an image of a Cat and 0 for everything else? How would the training set look like? I mean,...
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How does “ Sparsity of connections” in CNNs causes the network to have less parameters?

I am studying Andrew NG's lectures on Convolutional Neural Network and he had provided two reasons for CNNs having less parameters compared to Non-Convolutional networks . They are : Parameter ...
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What is the meaning of each element in input_shape of Conv1D in Keras?

I have a time-series data for 3 classes (each class is 35 second) as I extract each 1 second for 95 feature extracting so my final data has shape (105,95) (rows for time and column for feature). I am ...
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Why are RNNs used in some computer vision problems?

I am learning computer vision. When I was going through implementations of various computer vision projects, some OCR problems used GRU or LSTM, while some did not. I understand that RNNs are used ...
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Training Xml file for object localization neural network

My question is how to pass in xml file to train neural network. I have been working with object localization neural network (CNN). Now I have done labelled the image and make all xml files. But the ...
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What does it mean when the shape of input images is (600,64,64,3)?

While attempting an assignment, I found that shape of the input image was (600,64,64,3). I thought 3 stood for the number of channels but it's listed as the 4th dimension. What does this mean? This is ...
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How can a CNN account for spectro-temporal constraints in neural data?

What are there the best ways to leverage the unique "geometrical" constraints of spectro-temporal signal representations (architecture, filter shapes, data augmentation, etc.)? For example, ...
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How to properly train CNN on Full Digital Mammography

I am trying to train my Convolutional Neural Network on full digital mammography images. Here are example dimensions from a random sample: (5832, 4104, 3). Which ...
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How to draw multiple matrices (with grid, custom color per cell) in 3D with raycast?

I would like to draw multiple matrix with ray-casting in 3D. More specific like this (source) I have seen similar figure in some paper (I forgot which one). I wonder how they can draw like this. If ...
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Pre-trained CNN model makes Poor Predictions on Test Images Dataset

I have tried using several a pretrained models (MobileNet) for multiclass predictions. There are 42 classes and the distributions of the images are even across the 42 classes. This is my code: ...
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Classification of moving pixels with convolutional neural networks

I have a data set with videos of moving pixels. Each video contains 32 frames, each frame is 32x32 with two pixels in white and the rest in black. I have binary labels for 800 of these obtained by ...
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Why the validation loss is 0 even if accuracy not 100%?

I am training a CNN to perform a binary classification and at some point I get validation loss 0, however the validation accuracy is 84.5%. Why does this happen? I use binary_crossentropy as loss ...
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1answer
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Separating styles of numbers for simple digit classification

I am just getting started with my first simple digit classifier, so my doubts are at a pretty low level. In every dataset of digit images I've seen so far, different variants of each digit are grouped ...
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CNN Multi-Class Model Only Predicts 1 class for all test images

I am trying to build a CNN model to predict 42 classes. I used pre-trained models for this. I used Xception. This is how I have imported my dataset: ...
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Why my training and testing set are about 99% but my single prediction does wrong prediction?

I have performed fruits classification using CNN but i am paused at a point where all things are going right confusion matrix accuracy score all are correct it seems there is no overfitting but it ...
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How to deal with severe overfitting in a UNet Encoder/Decoder CNN in a task very similar to image translation?

I am trying to fit a UNet CNN to a task very similar to image to image translation. The input to the network is a binary matrix of size (64,256) and the output is of size (64,32). The columns ...
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Can features that are the same in every sample contribute to learning?

For simplicity, let's say that I am monitoring 4 sensors for an ongoing metric. The first column is the sensor ID and the second column is the sensor type. ...
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Train using the raw pixel images or training using facial embeddings

I want to create a model that can classify faces based on these face shapes(oval, square and heart). When creating my CNN model should I train it using the images as they are or should i generate the ...
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How do I get one overall prediction, where each data point has many pictures?

My task is not a simple image -> category. I have between 5 and 10 images of an object, and I must classify it. The problem is that the category isn't "...
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Should augmentation also be performed on the validation set when the dataset is imbalanced?

I am training a CNN on images (2 classes) and I have an imbalanced dataset (1:7 ratio). I am trying to tackle this by performing offline image augmentation. Should I perform augmentation also on the ...
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Why do we use 2D kernel for RGB data?

I have recently started kearning CNN and I coukdnt understand that why are we using a 2D kernel like of shape (3x3) for a RGB data in place of a 3D kernel like of shape (3x3x3)? Are we sharing the ...
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Can 1D-CNN method apply to real-time time series classification?

So I got an EEG dataset with shape (data points, 19), each row's shape (1,19) represent 1 second of EEG. I read much research on EEG classification that used many Deep Learning method and 1D-CNN is ...
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Training a CNN on a large dataset

I am currently trying to build a CNN for around 100,000 images. There are 42 classes. I have used the default batch size of 32. This is how my model looks like: ...
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Does a CNN think things inside the filter are collocated aka dependent on each other?

I am running a 1D CNN on tabular data. The rows are data that I have are not sequential, that is to say they are not part of a time series or ordered string, which is why I am not using an LSTM. So ...
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What is an optimal local sparse structure of a convolutional vision network?

I was reading the InceptionNet Paper, where I found quite a few references to developing a sparse network structure, but I am not clear on what this means. An ...
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MNIST data shape

In going through the different tutorials on CNN, auto encoders and so on I trained my self on the MNIST problem. The different images are stored in a 3D array which shape is (60000,28,28). In some ...
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Can features passed into Conv1d layers be randomized?

If I have input time series data shape as such, X.shape = (batch_size, 50, 5), it means that the data has 5 "features", each having 50 time steps. Does passing data like this into a Conv1d ...
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1answer
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ANN Classifier for extracted discrete image features

I have a features extraction algorithm that works well to extract features from images. I want to develop an ANN to classify those images based on those features. I have extracted features in a csv ...
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how to implement squared hinge loss in pytorch

does anyone have any advice on how to implement this loss in order to use it with a convolutional neural network? Also, how should I encode the labels of my training data? We were using one hot ...
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CNN Training accuracy shown during model.fit is not matching the predictions obtained on train data using model.predict

I'm using RESNET50 to classify images into 3 classes. The distribution of the classes is: Class_0 : 43% Class_1 : 32% Class_2 : 25% The training accuracy shown during model building process is ~80%, ...
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1answer
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Why does the model make good predictions for augmented images and not for the original ones?

I am training a CNN using maps images and I have performed offline augmentation operations (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM, ROTATE_90, ROTATE_180, ROTATE_270, TRANSPOSE, TRANSVERSE) on the whole ...
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Tensorflow / Keras Python CNN

I'm doing a project where a python script used a convolutional neural network to determine if a plant is healthy, and then water it based on that. While training the CNN, it seems to get up to 100% ...
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Implement the following loss function without interrupting the gradient chain registered by the gradient tape

I have spent five days trying to implement the following algorithm as a loss function to use it in my neural network, but it has been impossible for me. Impossible because, when I have finally ...
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Possible?: Split pdf file on pages where object detection algorithm finds custom object

I have scanned documents in large pdf files consisting of many individual documents. Each document begins with a exhibit number sticker, much like this one: example. The files are scanned in greyscale....
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Validation accuracy greater than training accuracy in cnn

I've splitted my training set in the ratio 80:20 and have developed cnn model with a dropout of 0.5. I'm getting an accuracy of 98%. But the validation accuracy stays greater than training accuracy. ...
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1answer
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CNN model contains several images that are null

I'm using a deep CNN with the ReLU activation function. When visualizing the layers (each with 32 filters), several of the filtered images are zeros. I am trying to reason why this may be happening? ...
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1answer
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Can I train a CNN to detect the number of objects without localizing them first?

So I was trying to search but couldn't find any answers. I was wondering if it possible to train a model to detect the number of items of interest in a photo without having bounding boxes or dots to ...
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Maximum number of images to train a convolutional neural network

I just wondered if there is a technical limit on the number of images to train a neural network. I want to work with extremely high numbers of images, around 1,000,000 to 10,000,000 images. Is there ...
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What is long range dependencies in CNNs?

I've got a school assignment that's roughly like this: Is CNNs efficient for detecting long-range dependencies in an image? I have no clue about this and I can't seem to find a good answer in the ...
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How to verify a CNN encoder works as expected?

I am using CNN as a part of kernel warping. The purpose here is to reduce input dimension (from N*M to K *1). The input data is not image data. I suspected that the CNN network might not work as I ...
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Don't know how to preprocess my dataset for image classification

I'm trying to do image classification using CNN. The exact model isn't important but I decided to try use AlexNet and I'm getting abysmal accuracy. I believe the issue might be with my data ...
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CNN Architecture comparison standards

I want to add comparison of accuracy section in my study report on CNN Architecture for a medical data. I have already added the comparison by VGG 16, AlexNet etc. Is it a standard to compare the ...
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Ensemble Model to Handle Different Image Attributes

I'm working on a project where I have images annotated across several attributes, say X, Y, Z...
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Implementing training in PyTorch

I wish to accomplish the following task in PyTorch- I have the COCO dataset, wherein each data sample is used in training YOLO v3. After being processed by the model, the sample is to be deleted if ...
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How to understand what CNN kernels correspond to what visual effect?

How to understand what kernels correspond to what visual effect? E.g. I see edge detection kernels and sharpening kernels and others, but I don't really grasp, how to interpret from the kernel matrix,...

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