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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18 views

after overcoming the overfitting, how to increase training accuracy?

I am building a CNN using keras for a classification task. I started with a simple model as a starting point and as almost all ML problems go, especially if the dataset is not very big, I faced an ...
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Loss & accuracy curves from learning rate range test interpretation

I am working on a project doing experiments with the Learning Rate Range Test (See "A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and ...
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Identify areas within a shape/polygon with Vision / ML

Given a shape, in the format of a binary image, I would like to detect and subdivide it to new areas. Below is an attached example of such a shape and the expected outcome where each new area is ...
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Improving accuracy of 2D CNN with time series classification

After somewhat extensive optimization of hyperparameters, my test accuracy remains at around 70 %. I have tried techniques to augment time series but they only make things worse. Unlike image ...
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1answer
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What does Keras image generators do with input images samplewise_std_normalization= True?

I have trained a a convolutional network samplewise_std_normalization=True. Now I want to check my model in real-time using Opencv. Therefore I would like to perform the same preprocessing on the ...
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Sequential batch processing vs parallel batch processing?

In deep learning based model training, in general batch of inputs are passed. For example for training a deep learning model with [512] dimensional input feature vector, say for batch size= 4, we ...
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1answer
22 views

Reducing Validation loss for Triplet Loss Embeddings

I'm trying to create a facial recognition detector using triplet loss followed by a kNN algorithm. I have roughly 10000 input images with 3 different classes, input size is 80x80. Model structure uses ...
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ValueError: `validation_split` is only supported for Tensors or NumPy arrays, found following input: RaggedTensor

I have following inputs to be trained on a CNN: x = np.array(Images) y = [ [[0]], [[76., 5., 9., 1., 0., 0.], [54., 4., 10., 51.]] ] Since the ...
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1answer
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Tensorflow parameters for CNN

I created the below simple model (taken from a Coursera course). It has a total of five convolutions. ...
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Transfer learning on images with higher dynamic range

Is it possible to fine-tune a CNN-based model previously trained on images with 8 bits depth [0 ~ 2^8] to fit a 16 bits depth [0 ~ 2^16] images? if there is any research paper that confirm that, it ...
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Multi site/source and Multivariate time series data (with multi time step) input in LSTM for forecasting

I am trying to make a multisite multivariate LSTM forecasting model with Keras. I have a simple Multivariate data structure like 3 X variables and 1 target variable with time-step 10, so my input ...
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32 views

Imaging multivariate time series for 2D CNN classification

I have multivariate time series data in the shape of (batches, timesteps, features). So, for 10 samples with 20 timesteps and 4 features, my dataset shape is (10,20,4). I have been using this data for ...
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1answer
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Image Preprocessing [closed]

I'm working on a use case where I need to pre process the image for my AIML model evaluation and I want to count all black pixels in RGB image. Instead of iterating rows*column, I'm looking for some ...
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COVID-19 Rapid Test Result Image Detection

I fine-tuned an Inception V3 model provided in AWS SageMaker to detect COVID-19 Rapid Test Results (see the image below for an example). I provided about 20 pictures of negative and about 20 pictures ...
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What is the input to CNN in this image segmentation paper?

I am doing a project on polyp segmentation in colonoscopy images. I have recently read the paper Polyp-Net: A Multi-model Fusion Network for Polyp Segmentation provided here. I have a few question ...
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1answer
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How to custom conv2D layer Keras using calculated values

This is my first question, Hello World I guess. I need to create a conv2D custom layer (at least, I think so), which should use my custom module for extracting values in the first layer. It would be ...
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LSTM and CNN - feature engineering and order for time series classification

My questions are related to multivariate time series classification, hence it may differ from forecasting problems. I can have either variable (entire history of the series) or fixed time steps (...
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2answers
45 views

Can't use The SGD optimizer

I am using the following code: ...
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How to check for vanishing gradient in CNN?

Is there is a way to check for vanishing gradient for CNNs using Keras? Like, for example, drawing the weight distributions for each layer and seeing if they are vanishing to zero?
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1answer
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Test data accuracy from real world have lowest accuracy than validation data collected in simulation environment

Background: Problem type: Multi class classification The dataset contains around 1,000 samples (simulated dataset of sensor signals), where each sample is 2D i.e (1000 * 1000 * 8). Additionally, I ...
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1answer
31 views

Activation Function

I am very new to machine learning and made an experiment myself. I have a few questions: Can I use $Y = sin(x)$ or $Y = 2x$ as an activation function for a neural network? Is it necessary to increase ...
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How could I implement a PointCNN with Tensorflow?

I'm beginner in DL, I would like to train a PointCNN on 50 epochs I'm working with the Mnist data on jupyter colab I found the official repository (link:https://github.com/yangyanli/PointCNN). I clone ...
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Encoding technique used in Keras ImageDataGenrator class

I would like to know what encoding is used by ImageDataGenerator for encoding the class labels. I have done a lot of research and found that there is a variable called ...
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23 views

Model Performance is Fluctuating

I am training a 3D u-net model for 3D medical images. My training data has 800 images, the validation data has 200, and test data has 200 images. when I try to fit the model, there is a fluctuation in ...
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1answer
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Problem with CNN [closed]

I am using the BreakHis database. More specifically, I am trying to classify the 400X images. The sizes of the images are $700x460x3$. Here are the details of the dataset. Also, here is the code for ...
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1answer
23 views

How to eliminate Non-Trainable params in Deep Learning [closed]

First of all, I would like to know what is the cause of Non-Trainable parameters? Secondly, how do you eliminate them? I used a combined CNN-RNN, it returned that 130 Non-Trainable parameters. Thank ...
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15 views

formatting data from pandas into CNN-compatible array

I have a pandas dataframe of this form: time x y 0 a b 1 c d For my CNN model training, I want to slice the first 5 rows (from t=0 to t=4) and apply my custom function to label this sliced set, ...
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1answer
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How to decide the padding size and stride size in CNN

In CNN in 2d, what situation is the size of the padding and stride changed in? So far, I could make sense of the basic concepts with padding and stride. Padding and stride can be used to adjust the ...
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What does the Region Proposal Network output in Faster-RCNNs?

Does it output corrections and offsets to the anchor boxes(that were generated by using some specific aspect ratios and scales)? Also if this the answer is YES, Suppose I have 3 scales - [8,16,32] and ...
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Attention mechanism: Why apply multiple different transformations to obtain query, key, value

I have two questions about the structure of attention modules: Since I work with imagery I will be talking about using convolutions on feature maps in order to obtain attention maps. If we have a set ...
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1answer
111 views

Train-test split and augmentation strategy for small dataset for video classification problem

I have a small data set of videos of approximately 100 videos for each class for a binary classification problem. This results in a total of 200 videos. I am applying two types of augmentations on the ...
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1answer
24 views

How Are Kernel Weights Trained in 1-D CNN's with Multi-dimensional Input?

I have far from a perfect understanding of how 1-D convolution neural networks learn, but I think I understand how the kernel operates on 1-D input data. How does 1-D convolution work with multi-...
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14 views

How to calculate Efficientnet's compound scaling

I would like to use compound scaling to tweak my own model, but I am confused about how to utilize the $d=\alpha^\phi,w=\beta^\phi,r=\gamma^\phi$ in compound scaling and how to compute the specified ...
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59 views

To freeze or not, batch normalisation in ResNet when transfer learning

I'm using a ResNet50 model pretrained on ImageNet, to do transfer learning, fitting an image classification task. The easy way of doing this is simply freezing the conv layers (or really all layers ...
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Mobile or web app to standardize image collection

Is there an app that I can use to unify / standardize how I take pictures to form a data set? Assuming that the image size, object location are, maybe picture quality are of importance for a CNN ...
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Systematically finding a CNN architecture?

I am trying to train a classifier from 25k images and 7k classes. Seems like my model overfits just after 3 epochs. I have tried to reduce the model complexity and increase the weight decay but still, ...
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How can I use squeezenet in keras with tensorflow 2.x backend?

I'm wondering if one can train a model outside of what is currently offered from tf-keras like squeezenet, shufflenet etc. etc. Has anyone had experience of doing so?
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1answer
32 views

Using MFCC and MFCC Delta features with a CNN

A lot of studies feed MFCCs as well as MFCC delta and double deltas directly to a CNN for audio classification. My question is, are the MFCC Deltas concatenated with the MFCC matrix? Most papers ...
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1answer
44 views

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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21 views

LSTM decoder with 2d's input

I am developing a CNN-LSTM autoencoder in pytorch to predict time sequences. The CNN input is a RGB image: RGB image => tensor[Batch size= 4, channel = 3,width= 256, height=256] and the output is ...
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1answer
41 views

Vanishing gradient problem even after existence of ReLu function?

Let's say I have a deep neural network with 50 hidden layers and at each neuron of hidden layer the ReLu activation function is used. My question is Is it possible for vanishing gradient problem to ...
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13 views

Does Fast-R-CNN model take into account the context?

Does Fast-R-CNN model take into account the local context and global context of objects in an image ? If it doesn't, is there any other models that does that and which is efficient in small object ...
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9 views

Understanding how anchors are created in a regional proposal network

I understand that in Faster R-CNN, the image is fed into a pre-trained CNN (such as VG16). So say I have a 37x50x512 feature map. Firstly, I assume that each feature map (37x50x1) is fed into the RPN? ...
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21 views

Don't understand Channels in Covolutional Layers [duplicate]

I'm struggling to understand the concept of 'Channels'. What does a channel mean in the context of an image. I understand that a grey scale image only has 1 channel, and a RGB has 3, but then I see ...
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Semantic Segmentation: Data formats

I am new to semantic segmentation and have a lot of problems with the things you do not see in tutorials: How the data should "look" at any given step. Currently, I have a U-Net which is ...
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1answer
25 views

Noise free image dataset [closed]

Presently, I am working on image denoising using CNNs. I am curious where I can find a noise-free image dataset? I am looking for real-world images but not the dataset that belongs to MNIST.
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14 views

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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20 views

How do convolutional layers in a CNN feed forward when there is multiple input feature maps?

I've been trying to recreate LeNet 1(LeNet 1 architecture is pictured in the top diagram) in python using NumPy. I am unsure of how the forward pass works when there is multiple Input feature maps in ...
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65 views

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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How will we handle Deep learning models when we have a low amount of data

How will we handle Deep learning models when we have a low amount of data either image data or text. I am not very familiar with these things. what steps to take when we train the deep learning ...

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