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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ResNet50 + Transformer
In many papers people extract features from image using ResNet and than pass them through transformer. I want to implement the same. I want to get features and than classify them using transformer. ...
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How does ResNet bottleneck architecture's input size is possible to change from 56x56x64 to 56x56x356?
In ResNet papaer, First residual block's input size is 56x56x64 caused by 7x7x64 filter in first layer. But, in the paper, they showed residual block that has 56x56x256 input size. How does it is ...
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Training Machine Learning Model - Neural Network - Islands Problem
I was working on the following leetcode problem:
Given a 2d grid map of '1's (land) and '0's (water), count the number
of islands. An island is surrounded by water and is formed by
connecting ...
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Preventing fitting Regression CNN to the mean when dataset has only few outliers
I am trying to train a CNN for regression on a dataset where most of the points lie around a similar output value. There are however a few outliers that are very important but they are less ...
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How does Pooling Layer in CNN introduce invariance to other transformations besides translation
Here is a quote from deeplearningbook which I am trying to process. I am not sure what do they mean by this quote, can someone help me understand please?
Pooling over spatial regions produces ...
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How to determine the number of the training images in Keras after data augmentaion?
I want to create a CNN model and I am using data augmentation. I want know the number of augmented images in Keras.
How to determine the number of the training images in Keras after data augmentation?...
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Keypoint detection from an image using a neural network
I am trying to design and train a neural network, which would be able to give me coordinates of certain key points in the image.
Dataset
I've got a dataset containing 1800 images similar to these:
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How to create anchor-positive and anchor-negative pair for feature X in a signature data set for training Siamese network
How to create anchor-positive and anchor-negative pair for feature X in a signature data set for training Siamese network?
Im have a cedar signature data set with 55 peoples signatures(classes) with ...
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Why RANDOM noise images always predicted as BIRD?
Say I have fine-tuned a 10-classification ResNet18 network on CIFAR-10 and the accuracy on validation set is about 93%.
However when feeding into 5000 random noise images (Gaussian noise with the ...
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Computing derivatives for backpropagation across a convolution step
This will be a long post, but I hope it'll be instructive to anyone else in my position. I'm trying to find how the derivatives of the loss function are calculated with respect to the kernels and ...
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Bounding box regression in R-CNN
In R-CNN paper, they give the definition of the target values for bounding box regression
Given that $(P, G)$ is a (prediction box, ground-truth box) pair of the form $(x, y, w, h)$ where $x, y$ is ...
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convLSTM : how to structure input data
I have the following dataframe containing training data that I have been using to perform a regression task using CNN + FC :
...
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LeNet-5 - combining feature maps in C3 layer
Famous LeNet-5 architecture looks like this:
The output of layer S2 has dimension: 10x10x6 - so basically an image with 6 convultions applied to it to derive features.
If each dimension was again ...
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How to fetch text from pdf to further proceed with question answer based model from the same document?
To illustrate the above title.
Suppose you have a pdf document, which is basically scanned from hardcopy, now there are set of fixed questions to answer from the document itself.
For an example a ...
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Identifying computer scanned digits
I have digit images as below which I would like to identify:
Some are of slightly worse quality :
The images are not of a fixed resolution but are mostly in the range (80*20 to 130 *40).
Due to ...
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Formula to calculate size of Capsule output similar to the formula for CNN?
Is there any formula to find the output dimensions of a capsule network similar to that of a Convolutional Neural Network?
For Example: In CNN, we know that
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Deep CNN with variable number of classes and "vanishing" data
I am using a deep CNN to predict the class an image belongs to (N classes). However, the number of classes is not stationary. I.e. over the time the network will be used, some new classes may emerge ...
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Input shape of dataset in hybrid CNN-SVM classifier
I am working on hybrid CNN-SVM for classification task, where I aim to use CNN for feature extraction and SVM for classification.
So after the training of my CNN model as below:
...
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Which model is used for document extraction (CamScanner, Microsoft Lens etc)
I want to start a small project where I'd create a model(s) that would extract document from a picture and rescale it, something like CamScanner or Microsoft Lens apps do.
I've gathered a small ...
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Can Darknet be Integrated with Tensorflow and tfx?
Tensorflow and Darknet are deep learning frameworks that work and are configured. Can Darknet be Integrated differently? Is there any framework or way to integrate Darknet within a tfx pipeline, for ...
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Applying GradCam to video classification models
In the original paper, it says that GradCam visualization can be applied to any convolution based model. The problem is stated for convolutions that process images. In my case, I am classifying videos ...
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why transpose convolution is called "transpose"
https://d2l.ai/chapter_computer-vision/transposed-conv.html
https://en.wikipedia.org/wiki/Transpose
I understand what transpose convolution does, but I am confused about the name of 'transpose'.
In ...
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Methods to visualize the filters in the later layers of a CNN?
I've extracted the weights from the filters of a pretrained model (AlexNet). I wish to represent these weights visually, this works fine for the first layer as there is only 3 input channels so I can ...
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Deep learning test loss curve won't go down
I've been working with Deep Learning projects for this current project that I am working on and it's basically a time series classification problem. Where given an array of time series data I need to ...
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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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Train and valid accuracy and loss stay the same over many epochs of training with Pytorch
I am building a neural network to recognize hand gestures from the leapGestRecog data set from Kaggle. While training I ran into some issues.
Here are some images of my data set:
I augmented my data ...
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out of frame landmark detection with CNN
a robust landmark detector must cope with occlusion (the landmark surrounding are occluded by another object) the detection can still be performed but it should be stressed that the landmark is ...
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Why does my CNN validation loss increase immediately, even with lots of data?
The Issue
I've been working on a regression CNN implementation to predict time series data and have run into an issue where my validation loss and training loss diverge immediately during training, as ...
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Dealing with high dimensionality datasets
I have data of dimensionality (25000, 100, 500) i.e. 25000 rows each consisting of a 2 dimensional 100 X 500 matrix. Currently I am only applying CNN for ...
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Help required in understanding how the error of a convolutional layer is calculated when filter and delta of next layer have differing dimensions
I am trying to implement a CNN in NumPy so as to better understand its inner workings
My architecture is as follows
10 images with 1 channel and with 28-pixel rows and columns (Dimension : (...
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Getting confusion matrix with Keras flow_from_directory
For a homework I have to analyse a set of images. For this I plan to use convolutional neural network.
The images are split onto specific folders :
A test set with 624 photos
dataset/test/normal (...
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Understanding CNN by visualizing class activations using GRAD_CAM
I followed the blog Where CNN is looking? to understand and visualize the class activations in order to predict something. The given example works very well.
I have developed a custom model using ...
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In Fast R-CNN how are input RoIs mapped to the respective RoIs in the feature map before RoI pooling?
I've been reading the Fast R-CNN paper
My understanding is that the input to one forward pass is the whole input image plus a list of RoIs (generated by selective search or another region proposal ...
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Keras: DepthwiseConv3DTranspose or doing transposed Conv. with a Conv. layer
I am building an autoencoder for 3D images and would like to use Depthwise convolutions.
For the encoder, I found an
implementation of a depthwise 3D convolutional layer (DepthwiseConv3D).
For the ...
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What doese 'v' mean in GoogLeNet?
In GoogLeNet (this link), there is 'v' notation in Figure3 like '1X1+1(v)'.
I don't know the meaning of 'v'.
Also, I understood 's' as stride. But, I don't know the reason why plus operation is used ...
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Size difference during backpropagation between fully connected layer and convolution layer?
This is a simple example of a network consisting of two convolutional layers and one fully connected layer.
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Load test data to pass into predict_classes()
I am using the Sequential model to train a CNN. The test data are in the submission_data_path as .png images.
Is it possible to ...
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How to solve warning : ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data?
I am doing image classification and I have a training set and a test set with different distributions. So to try to overcome this problem I am using an Image generator in the following way:
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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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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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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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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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List of CNN for Emotion/Sentiment recognition on images with performance on main datasets (IAPS, GAPED, EmoPics, NAPS)
There are more and more databases of pictures classified or rated with emotions.
For instance, I know of 4 databases (IAPS, GAPED, EmoPics, NAPS) rating pictures on 2 dimensions: Valance (positive vs ...
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Why do UNET-like architectures perform better than sliding-window approaches?
I'm writing a thesis that heavily focuses on semantic segmentation of biomedical images.
I'm reviewing different segmentation approaches, identifying two main approach branches:
A sliding window-...
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Does high accuracy metrics with small (but equally sampled) dataset means a good model?
I have been training my CNN with 200 images per class for a classification problem. There problem is a binary classification one. And with the amount of test data ( 25 per class) I am getting good ...
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Transfer learning on yolo using keras
I am working on a project that uses object detection. I have logo images that need to be detected in a video. I am doing this in keras. I followed this blog to convert the yolo weights to a keras ...
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How to put data into a 1-dimensional ConvLSTM2D with keras?
I am attempting to adapt the frame prediction model from the keras examples to work with a set of 1-d sensors. I have android wearable sensor data and am designing an algorithm that can hopefully ...
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Trained vs randomly initialised cnn model. Difference in inference speed
We have 2 models written in Keras. We then convert them to coreml format and test the inference speed of each one on iPhone 7. The model architecture is the same for each of the two. The 1st model is ...
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Audio files and their corresponding spectrograms for image classification process
Suppose I have a dataset of audio files that I have to use for whale sound classification. I am choosing the strategy of treating it as an image classification problem by using their corresponding ...
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Model for classifying time-series data with distinct features?
I've heard about time-series classification being done with TCN's and CNN's combined with LSTM's very often, citing that CNN's would provide insight both forward and in the past since you already have ...
