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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Distributed inference for image classification

I would like to take the output of an intermediate layer of a CNN (layer G) and feed it to an intermediate layer of a wider CNN (layer H) to complete the inference. Challenge: The two layers G, H have ...
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Underfitting issue

I have a small datset (530 images) trained on a simple CNN called AquaSight. This is the architecture. I had an underfitting problem, 75% accuracy and 0.6 loss. How can I solve the underfitting ...
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Validation Accuracy Not Changing

As the title states, my validation accuracy isn't changing when I try to train my model. I've built an NVIDIA model using tensorflow.keras in python. I have absolutely no idea what's causing the issue....
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Extracting features from bounding boxes of CornerNet

I am using the CornerNet model. I want to extract features from specific bounding boxes that have been detected. Unlike Faster RCNN,...
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Neural network weight initialization

I was working on recreating the Convolutional Neural Network Le-Net 5. I was getting around 96.5% accuracy on the training set. This was not near the 99.2% the network was meant to be operating at. ...
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Prune Neural Networks layers for f% sparsity - TensorFlow2

I am using TensorFlow 2.5 and Python3.8 where I have a simple TF2 CNN having one conv layer and an output layer for binary classification as follows: ...
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Can I use a different image input size for transfer learning?

Most pre-trained CNN models accept a $224x224$ input size when they were trained. Can I use $256x256$ to get a higher accuracy?
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Do grouped convolutions actually improve learning?

My Understanding of Grouped Convolutions Let say we have some data with the dimensions [100,100,32] (lets ignore batch size and assume channels last) and we want to ...
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Comparing results of different image splicing methods on a part of the CASIA 2.0 dataset

So I am working on an image splicing detection algorithm using ResNet-50 model. I am using the CASIA 2.0 dataset which consists of 7491 Authentic images and 5123 Fake images. However out of the fake ...
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Does a rotational convolutional filter exist in neural networks?

Traditionally, a convolutional filter is one where you take a matrix of numbers, multiply it with a subset of the data, and then sum it up. Then you move the filter left to right and top to bottom in ...
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How does the character convolution work in ELMo?

When I read the original ELMo paper (https://arxiv.org/pdf/1802.05365.pdf), I'm stumped by the following line: The context insensitive type representation uses 2048 character n-gram convolutional ...
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How can I design a search space for this simple problem?

My goal is to predict the convolutional layer execution time and I am trying to build a dataset for predicting execution time. The following parameters are used as an input to the regression model to ...
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CNN training exhibits higher accuracy after fake quantization

I have trained a convolutional neural network for image classification using quantization aware training provided by tensorflow model optimization. I first trained the model without quantization, and ...
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Squeeze and excitation blocks in 3D convnet architectures to forecast physical systems

I am using a temporal 3D U-NET (time dimension + 2 spatial dimensions) to forecast physical features of fluid (pressure, temperature, and velocities) using data from a simulator. I am thinking of ...
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1D CNN time series classiifcation : ValueError: Shapes (10, 10, 8) and (10, 8) are incompatible

I'm working on a time series classification using ASHRAE RP-1043 chiller dataset which has 65 columns and more than 3000 rows for each chiller fault and normal condition. And I have used 1D CNN and ...
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CNN heatmaps substantially different for different input images

I have a convolution neural network for regression, where medical scans of many people are trained to predict some continuous variable (body related phenotype). I get reasonable performance (R2 ~ 0.9)....
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Siamese Network for face comparison wont learn, accuracy stuck on 0.5, and loss stuck too

I'm trying to train a siamese network which contains a CNN and an embedding layer at the end to yield 2 similar (close) vectors for 2 images of the same person. I'm using the LFW_Cropped dataset, and ...
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Specify torchvision transforms depending on the properties of an image and a mask

I have a dataset 1000 of images and corresponding segmentation masks from dermatologists. The images come in different sizes (as low as 400x600 and as large as 4Kx4K). 95% of image pixels are not ...
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Why first fully connected layer requires flattening in cnn?

One can read everywhere on internet or in books that in convoluted neural networks, between convolution layers and the first fully connected layer, you should flatten your data. I managed to ...
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Can you use fully convolutional networks for binary classification?

I know that fully convolutional networks can be used for image segmentation and similar but I wondered if you could also apply them to simple image classification tasks. And if so, what is the proper ...
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Regress values inside the bounding boxes to predict a value in Object Detection

I am currently working on an object detection task. I have a dataset of grayscale and depth images. The annotation format is $x_1, y_1, x_2, y_2, class, depth$. I calculated this depth (of each ...
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Studying and choosing between different neural network structures

I would like to develop a model that uses convolutional neural networks for image classification. From the many different network structures described in papers and articles online, I would like to ...
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Convolutional autoencoder - why keras example is asymmetry model?

I'm looking on keras convolutional autoencoder example, and confused with the model structure: ...
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ValueError: Input 0 of layer sequential_7 is incompatible with the layer

I have 77 columns, with 4 class labels (already one-hot-encoded) by get_dummies. ...
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1answer
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Can landmark detection be only used for faces and human bodies?

I want to use landmark detection for finding specific points of interest in an indoor setting e.g. bedrooms, bathrooms etc. Is it possible to use it? So far I have only seen landmark detection being ...
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How to evaluate the performance of CNN model when val_acc changes everytime I ran the code?

I have a CNN model and am trying different feature extraction techniques on my data and passing it to my CNN model in TensorFlow. Let's say I chose SIFT as my feature extraction technique and trained ...
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Will training with yolov4 backup weights early cause weak prediction or is a true backup?

I am training a dataset in yolov4 using the repo from AlexeyAB darknet. In his repo, backup weights are created every so iterations but you originally train with a pretrained weights file. I was ...
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Average loss is 0 when training dataset with darknet yolov4

I am currently training a dataset using yolov4 darknet from AlexeyAB Github found here: https://github.com/AlexeyAB/darknet The dataset I am training is called FishNet Open Images. The dataset has 86,...
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Transfer Learning or Custom Network?

I am learning Computer Vision and I was wondering if it's usually worth it to build a custom convolutional network from scratch (through trials and errors) or if using transfer learning with a popular ...
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Neural Networks for multivariate regression: can an additional output “help” the training?

I am addressing a problem of multivariate regression by using a CNN. In particular, I have a data set of artificial images which have been generated by a physical model which takes in input, suppose, ...
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Large gap between validation_accuracy and validation_binary_accuracy in Keras

I am building a convolutional neural network in Keras to try to predict binary classification of some text sequences. ...
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Convolutional Neural Network CNN pixels support the last layer

I'm studying for a computer vision module and I'm on the deep learning topic, in one past paper we have the following question: Given that a convolutional neural network has five convolution layers (...
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Number of additions in Conv2d

I am trying to estimate the number of operations during a forward pass of the Conv2d operator and validate the estimation by using the perf tool. But although I managed to get the right number of ...
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CNN + LSTM model for images performs poorly on validation data set

My training and loss curves look like below and yes, similar graphs have received comments like "Classic overfitting" and I get it. My model looks like below, ...
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What is the explanation of convLSTM 3x3-256-2?

I have read it in a paper, convLSTM 3x3-256-2 means convLSTM with 3x3 filter size, 256 hidden states, and 2 layers. But the original LINK do not show any argument regarding hidden states and layers. ...
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What is the intuition behind transposed conv layers being able to upscale images?

I was reading the ZF Net paper and it used the term Deconvnet on some searching it seems this is the wrong term and rather we use transposed convolutions instead. I understood how transposed ...
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What should be the input shape for convLSTM if ResNet-50 is applied before?

I have a video dataset, extracted all its frames, and applied ResNet-50 to extract features from all frames. ResNet-50 provides feature map of (2534, 7, 7, 2048), 2534 are the number of frames. Now I ...
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Why trainable parameters are not considered right?

I have tested the "ResNet" block and it works fine, but when I call it in the model class, it somehow it does not work properly? Is it related to the model definition?
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Train/Validation/Test split and K-fold Cross Validation

I have a dataset that I have split in train, validation and test subsets. I want to evaluate several CNN architectures and hyperparameters so I have trained several models with different ...
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What should be the input shape for convLSTM if ResNet-50 is applied?

I have a dataset 12 videos. Each video is comprised of 179 frames. On these frames, I have applied ResNet-50 to extract features, and I received (179,7,7,2048) features. As far I know, 179=Total ...
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What model to use for relative comparison between 3 figures?

I am working on a problem where I am given three images of different dishes (A,B,C) and the task is to figure out if figure B or ...
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26 views

Backpropagation on a CNN

I have tried searching for this, but it seems like no one addresses a key aspect of this problem (or maybe I'm overthinking this): So, first let's assume we have a 3x3 image with a single channel, and ...
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High image segmentation metrics after training but poor results in prediction

I'm trying to build a model with Keras that predicts four classes of features from microscopy noisy images which cover about 10 - 30 % of the image. I'm using U-net because my dataset is small (150 ...
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Image classification problem using convolutional neural networks [closed]

I am trying to solve this problem by using a convolutional NN to classify an image data set to check the type of disease it is. I have reached task 1b and trying to implement the training loop. ...
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Deep learning detect reference boundary in text (or number of references in text)

I have several documents that either contain or don't an X number of references. I would like to build a model that can detect the number of references if any in a text. I've been thinking for ...
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61 views

Non Linearity used in LeNet 5

I was looking at the original implementation of LeNet-5 and I noticed a disparity in different sources. Wikipedia suggests that the non linearity used is the same sigmoid in each layer, some blog ...
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How to achieve causal deconvolution with Keras

Keras allows one to specify padding='causal' to achieve autoregressive connections in Conv1D layers. However, in deconvolution with Conv1DTranspose, padding is ...
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Weighting the loss function based on previous seen true positive rates

Similiar to class imbalance there is always something I would call "learnability imbalance" in multi-class classification. What I mean by that: Even when the classes are evenly distributed ...
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One hot vector output in classification task

I'm working on CNN model and I used one hot vector type of labels. The number of classes is 3: [1,0,0], [0,1,0], [0,0,1]. net(x) I'm getting such an output: [0....
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Checking trained CNN on the images

I trained my CNN (model) classifier and want to check it on some new images. I have image x, so this syntax works for me for one image: torch.argmax(model(x)) What ...

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