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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3 views

How to have Multiple classes in a single video?

I am building a swimming stroke classification system using CNN. let's assume each stroke contains 3 steps - ('Ready', 'Impact', 'finish'). I want to train a model which will predict whether the input ...
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Model always predict the same values

I am currently trying to build a 3D-CNN model to predict video quality, when i trained the model with a small dataset (300 videos) it predicts different values, but when i use a huge dataset for ...
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What is the architecture of Pose Net?

PoseNet is state of art approach , I am using it for a pose estimation . I read on Internet that pose net uses model like MobileNet or VGG etc , I want to know that is PoseNet itself a model or it ...
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Representing multi-channel input signals with a single signal

I am working on an EEG signal classification problem. My dataset consists of EEG signals stored as 19X30000 NumPy arrays. Each row represents a single channel. For now, I am converting each of the ...
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Testing accuracy very low, while training and validation accuracy ~ 85%

I have a training dataset of 10000 pictures and a test dataset of 15000 pictures. There are 23 types of birds. First of all, I imported the necessary ...
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Coordinates of cereals in figure

I'm trying to automatize cereal counting from an image. The problem is that I have only a few pictures and I don't know how to train a CNN to do the job. Should I create mini images with single ...
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how to fix constat loss,accuracy ,val loss and val accuracy?

i am training cnn model with loss=binary classification and i am getting this error .is there any solution? i tried different methods as changing loss function ,adding Dropout ,regularizers ,batch ...
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High Bias in 1dimensional CNN with high training Loss

I am working on a project where I have to classify the good and bad packets. I am modelling my data with 1 dimensional CNN u where i have to predict good packet(0) and malicious packets(1,2,3), i have ...
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CNN seems doing good during training and validation but not really

I'm doing a simple binary classification using this dataset ...
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How to calculate convolution for 2nd conv Layer in CNN, Do we need to average across all feature maps?

I understand that for the first layer (assuming we have a grayscale image) we calculate the convolution of 3*3 receptive field as a weighted sum of receptive weights with pixels $ x1 · w1 + x2 · w2 + ...
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Adding layer to a trained CNN to process higher resolution images. Tried 2 schemes, 1 works fine, 1 fails completely

I'm working with images coming from a sensor, for which 1 pixel corresponds to 2 mm in the real world. I've built and trained a CNN that does semantic segmentation of the image (128x128 pixels) and it ...
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1answer
24 views

Large variation in cross-validation scores [closed]

I'm training a CNN with 5-fold stratified cross-validation. On the first fold, my accuracy is ~80%, on each subsequent fold the accuracy is ~50%. Finally, upon fitting the entire training set my ...
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Image classification with CNN Python

I'm working on image classification using CNN, my dataset contains more than 50 classes (50 folders) which represent the types of car parts, and in each folder we have vehicle brands, each vehicle ...
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Best 5-layer pretrained CNN model

I am doing a visualization project on convolutional neural nets to aid learning and need a simple to display but complex enough pretrained CNN model so I can visualize feature maps for each layer. I ...
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1answer
34 views

Training loss = 0, training accuracy =1, validation and test around 85%

I have created different CNNs for doing image classification. The dataset is this: https://www.kaggle.com/crowww/a-large-scale-fish-dataset There are 9 classes, and each class contains 1000 images of ...
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1answer
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Max Pooling in first Layer of CNN

I am seeing, in all the notebooks that I found, that Max Pooling is never used in the first layer of a CNN. Why this? Is it a convention among data scientist to do not use max pooling in the first ...
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Understanding, visualizing and interpreting CNN activations

I am working with the first layer of a CNN and trying to understand how to interpret the activation output. My CNN takes input from 3 channels (RBG picture) and the first layer is ...
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1answer
24 views

Rescale parameter in data augmentation

I'm a little bit lost about the rescale parameter in the ImageDatagenerator function. I know that the rescale argument by itself does not augment my data and that ...
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1answer
15 views

3 images as one input in CNN (U-Net)

I have been advised by my supervisor that if my U-Net segmentation network has RGB images at the input then I could use the channels for different images - median filter for R, normalization for G, ...
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Split npz Dataset into Train/Test using Sklearn [closed]

I have a dataset of faces stored in an NPZ file that I would like to train it on Siamese Network. To do that, the dataset must be split into train / test using Sklearn. However, when I run the code to ...
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Cross-validation in CNN

I am trying to implement deep learning for image recognition, but I am still confused with cross-validation. Let's say I will use about 100.000-1.000.000 images in total for a binary classification ...
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Understanding scipy.signal.convolve2d full convolution and backpropagation between convolutional layers

I'm learning about convolutional neural networks. The convolution operation in order to extract features that is described in literature and posts used for this is quite intuitive and easy to ...
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Convnet with peculiar loss function not learning!

Im using this loss function: ...
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1answer
19 views

What is feature channels mentioned in U-Net?

I was reading the U-Net paper for medical image segmentation. I had a doubt in the architecture. The authors mention that the max pooling layers in contraction path double the number feature channels ...
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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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Selecting time window for very different feature lengths

I'm doing my first steps with python, keras and a 1D time series classification. I can train a model, save it, apply it to validation or test data. I'll build my first implementation around this ...
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2answers
47 views

sklearn package with AttributeError: 'MissingValues' object has no attribute 'to_list'

I am currently trying to reproduce this tutorial on building a CNN based time series classifier for human activity recognition. My setup is: Windows 10, Pycharm IDE with a new project for this ...
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Keras CNN always predicts the same output values, independet of the input

We train a Reinforcement Learning model that should always predict the next best action in each state. Somehow the training does not work since after a few games, the outputs for each of the actions ...
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1answer
54 views

Modifying U-Net implementation for smaller image size

I'm implementing the U-Net model per the published paper here. This is my model so far: ...
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write cnn structure in simple words [closed]

how to write following cnn structure in simple words for better understanding CNN with 11 layers 3×3×32 Convolutional → Dropout → 2×2 MaxPool → 3×3×64 Convolutional → Dropout → 2 × 2 MaxPool → Flatten ...
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U-Net doesn't work with images different from the dataset

I have implemented a very similar U-Net code from github, but for a different dataset, this one, to segment roads, it works fine using the test folder images, but when i for example, pick a print from ...
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How does time needed for training differ between different batch sizes?

I've constructed a CNN in Python using Numpy, which is trained with mini batch gradient descent for MNIST digit classification. When training with a batch size of 1, the time needed for 5 epochs is ...
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1answer
22 views

interpret cnn structure

I am trying to interpret the CNN model from the below settings. AS I am new to deep learning and I am not able to fully comprehend the layer structure . Could someone please tell me is these two ...
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1answer
19 views

Is my CNN model overfitting?

I'm training a standard CNN. Attached my training curve. My model: ...
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1answer
23 views

Implementing U-Net segmentation model without padding

I'm trying to implement the U-Net CNN as per the published paper here. I've followed the paper architecture as closely as possible but I'm hitting an error when trying to carry out the first ...
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1answer
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Why do my target labels need to begin at 0 for sparse categorical cross entropy to work?

I'm following a guide here to implement image segmentation in Keras. One thing I'm confused about are these lines: ...
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1answer
67 views

TypeError: Expected int32, got None of type 'NoneType' instead

I want my model batch size to be a dynamic shape, and I've assigned none as batch size, but that's causing an error. Here, in the first line, I specified batch size as None: ...
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19 views

First CNN and shapes error [migrated]

I just started to build my first CNN. I'm practicing with the MNIST dataset, this is the code I just wrote: ...
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1answer
54 views

How many neurons a deep learning model (specifically a CNN) does have?

I found total number of neurons of ResNet-50 model is 26,560 and 94,059 in two different papers. Their titles are below: 1: DeepXplore: Automated Whitebox Testing of Deep Learning Systems 2: Testing ...
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Should a filter learned within a residual block be different form its vanilla CNN counterpart?

I have a very basic CNN using Conv2d with multiple layers and activations, each layer $\ell$ has parameters $w_\ell$ inducing a mapping $f_\ell(x,w_\ell)$. Now I decide to introduce skip connections ...
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28 views

Convolutional AE always overfitting time series - what’s wrong?

I've build a CAE for anomaly detection in time series, but it is always overfitting. I've tried data augmentation, short/long inputvector, dropout rates... I don't know what I'm doing wrong, may be ...
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1answer
55 views

Deep learning with Imbalanced classes [duplicate]

I am trying to model a packet data with 1 dimensional CNN but I have a very imbalanced classes in my target. I have 3 classes as class 0 has 53000 cases, class 1 has 300 cases and class 2 has 150 ...
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1answer
37 views

Input shape of an Xception CNN model

I have a Keras Xception based model for gesture recognition. The accuracy of the model is around 60-70% for classifying 7 different gestures. The training dataset consists of 320x240 and 640x480 pixel ...
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1answer
18 views

Understanding Node Embeddings

I have only just started to look into graph neural networks and I am a little confused on the node embedding process. Here is my understanding, please let me know if i misunderstood: Given unlabelled ...
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1answer
16 views

Semantic segmentation in high-resolution images with high variance - cannot avoid underfitting

I am working on a dataset of 2K images for a semantic segmentation problem. I want to detect and localize small objects, with the smallest mask to be 5x5 pixels. The images include 5 different ...
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1answer
26 views

Why is my CNN not training

Hi I am trying to train a CNN to differentiate between pictures of dogs and pictures of cats. It does not seem to learn anything no matter how I change the architecture. I have used the following code ...
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2answers
70 views

Keras/Tensorflow: model.predict() returns a list. How do I match the output with my class names?

I have a CNN built in Keras. I have saved it and am now using the model.predict() function to make predictions from it. Whenever I run the following code, ...
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20 views

Search for redundant filters(channels) in CNN

When training a CNN one specifies in each layer the number of channels. In the input we have 1 channel for grayscale image and 3 for RGB image, and then usually the ...
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1answer
45 views

What is a sliding-window convolutional neural network?

In the abstract of "U-Net: Convolutional Networks for Biomedical Image Segmentation", the authors mention a sliding-window convolutional neural network. I've found several other articles ...
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51 views

Time series classification using CNN model, 1D or 2D?

I have a multivariate time series dataset that has the same length for each observation but looking at a different time frame (eg. One might be from January to May and another one might be from August ...

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