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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CNN wiht input in [0-1] range and ReLU. Won't some channels “die” at initialization? [closed]

I am writing a pretty standard CNN. I'm normalizing my inputs so that the lower value is 0 for each image (and the top value generally isn't higher than 1...). The first layer is a convolution and the ...
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Choose proper(best) input size for CNN model?

I have different size images in my dataset for training. How we would choose proper(best) input size for a model? What I do is choosing average width and height of all images. Is there better of ...
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What is the state of the art in deep learning for image feature representation? [closed]

I am relatively new to deep learning and want to build a model that extracts features from images which i can then use for classification or anomaly detection. I have searched for quite a while now, ...
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11 views

How to train the model with for loop instead of the built-in epochs

i want to use a for loop for epochs instead of the built-in ones. Does these parts are similar. 1) ...
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CNN model to predict if the shops are open or closed

I'm planning to train a model used to determine if a shop is open. Images are either shot by my students or scraped from the internet. They have manually cropped them so that only one shop is shown on ...
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How does Keras.Model() retrieve the different layers between its passed input and output layers?

I have recently been learning Keras and am trying to understand how the keras.Model function is able to interpolate the different layers between passed input and output layers. Do the input or output ...
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1answer
129 views

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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Can we use CNN to effectively learn from a table whose various rows are permuted in the testing dataset, output for each remains same?

I have a set of classes, 37 to be precise. Each class is represented by a feature vector of size [1,10]. A single input sample has the dimensions of ...
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1answer
29 views

EarlyStopping based on the loss

When training my CNN model, based on the random initialization of weights, i get the prediction results. In other words, with the same training and test data i get different results every time when i ...
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10 views

Representing a 2d-grid around an agent

I'm trying to train a neural network-based model to play a game similar to Pac-Man, except there's no maze. i.e., the player is in a 2-dimensional grid, with dots of food in some locations, and the ...
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1answer
19 views

EfficientNet function composition or Hadamard

In the page 3 of the paper of EfficientNet, there is a equation $$\mathcal{N} = \bigodot_{i=1...s} \mathcal{F}_{i}^{L_i} \big(X_{\langle H_i, W_i, C_i \rangle}\big)$$ where $\mathcal{N}$ is the conv ...
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Preparing training data for crop species classification from drone images

I have high resolution multispectral (R,G,B,NIR,RE bands) images of a field taken from MicaSense RedEdge mounted on a drone. There are various species of crops planted. I want to classify the crops or ...
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How to handle images of different sizes that are smaller than the input layer of a deep learning model?

I am performing human awareness detection and have trained my model using transfer learning with MobileNetV2. This model expects a tensor of dimension [Null,224,224,3]. I have applied face detection ...
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image regression in TF2 Keras

I am trying to figure out, how I could build a model which takes an input image of varying illuminations and outputs an image which has adjusted illuminations by a given label-image. I found out that ...
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2answers
31 views

CNN: How do I handle Blurred images in the dataset?

I have 30% blurred images in each classes. I have a total of 10 classes. I'm not allowed to drop these blurred images. How do I train the model to get better accuracy for both blurred and nonblurred ...
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Is it possible for a model with a large amount of data to perform very well and reach an extremely low cost within a single epoch?

I am working on a project to detect human awareness levels using this dataset. I preprocessed the video data as the following: Convert video to frames(taking a frame every 5 seconds. Rotate the ...
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1answer
33 views

Neural network regression is not dynamic enough to predict target range?

I'm working with a CNN on a regression task, using MSE as the loss function. Here's a plot of predictions vs targets for the training set. Note: Legend is wrong. Blue = prediction vs target | Red = ...
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Can Spatial Transformer Networks (STNs) be used to learn local image transformations?

in most of the applications of STNs that I have seen in research papers, the module is used to perform global warping, i.e. a single parameter θ is learned for the entire image. Recently, I tried ...
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How to Convolve a High-Res Image by a (fully convolutional) CNN kernel?

My CNN is an extremely simple neural network. ...
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1answer
23 views

How to backpropogate Convolution layer padding inputs with respect to output derivative

I created a convolution network with 5 Conv blocks, let discuss the issue based on Conv block 4 & 5 Conv Block 4 Input Image size : 28 * 28, Padding size 1 : 30 * 30 (image size ...
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1answer
16 views

PyTorchs ConvTranspose2d padding parameter

Im confused about what PyTorchs padding parameter does when using torch.nn.ConvTranspose2d. The docs say that: "The padding argument effectively adds dilation * (kernel_size - 1) - padding ...
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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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2answers
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Best way to implement 10-fold cross validation for CNN model

I was performing a binary classification problem with 15000 RGB images using a scratch build CNN model. While it comes to evaluate the model, I can do it in two ways: Splitting data Train and Test ...
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Regressive CNN visualization

I am interested in creating heat maps for my CNN which regresses images to a real value. However, most of the methods I have seen (eg, grad-CAM) were developed for classification. Is there a way to ...
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1answer
26 views

Output landscape of ReLU, Swish and Mish

I found the following figure in the original Mish paper (https://arxiv.org/abs/1908.08681). I understand that this figure describes how the loss is being changed, if the change is smooth or not. But ...
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1answer
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Debugging a simple 1-D CNN for solving a simple classification problem

I have a rather simple classification problem that I am trying to solve. Each instance in my problem is a list of 1024 bytes (each byte is represented by a digit between 0 and 255). There are 2 ...
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What is the difference between additive and multiplicative attention? [closed]

This paper (https://arxiv.org/abs/1804.03999) implements additive addition. I think the attention module used in this paper (https://arxiv.org/abs/1805.08318) is an example of multiplicative attention,...
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How to use a dataset with only one category of data

I am performing a classification task, to try to detect an object. A picture of the environment is taken, candidates are generated of this possible object using vision algorithms, and once isolated, ...
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1answer
25 views

Is there any possibility to apply deep dreaming in data augmentation?

I looked into the deep dreaming concepts and feel like this has the potential for data generation. But i'm not sure how possible this concept is. Any thoughts regarding this?
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1answer
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Unbalanced dataset on image classification, is it better to lose samples and balance it?

I am dealing with a binary image classifier. I'm using a CNN to predict if an image is positive or negative. The problem is that the positive class represents only the 2% of the total samples. In this ...
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How to improve the Tensorflow Image Captioning model?

I'm working on the Image captioning with visual attention tutorial from Tensorflow. With a few rounds of training, the tutorial shows bad caption results (I guess the goal of the tutorial is just to ...
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1answer
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CNN - confused about a question in Andrew Ng's DL course

In lecture, we talked about “parameter sharing” as a benefit of using convolutional networks. Which of the following statements about parameter sharing in ConvNets are true? (Check all that apply.) ...
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How to interpret a regression model performances (Loss, accuracy) under keras

I built a regression model using Keras. The following parms were used: ...
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TCNN vs Conv1D+LSTM

I was reading a bit about TCNN, just wanted to ask if someone has worked with it, can you tell that which is better and Why? 1d Conv + LSTM/GRU or TCNN.
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2answers
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Creating dataset - imbalanced or balanced?

I'm trying to make an image classification model and I have 5 classes - A, B, C, D, E. The goal is to get the highest possible classification accuracy. I have a database of images and I'm selecting ...
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1answer
29 views

Using CNN on one class only

I want to use InceptionV3 on one class of image (ie. to detect one type of object/one label only). For example, to detect 'penguin' or 'no penguin'. All my images have been cropped down to mostly ...
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CNN for Coordinates Prediction

I am working on a CNN to detect body parts (Right Shoulder, Left Shoulder), something more like Facial Keypoints Detection. I tried solutions from Kaggle as I think it's almost the same problem but it ...
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1answer
29 views

Is it ok to use photo collages as dataset instead of single images for training the object detection ssd model?

Is it normal/better to use photo collages (multiple photos in one image) as a dataset instead of single images for training the object detection ssd model? I am using Tensorflow Object Detection API ...
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15 views

Variational Autoencoder (VAE) latent features

I'm new to DL and I'm working on VAE for biomedical images. I need to extract relevant features from ct scan. So I created first an autoencoder and after a VAE. My doubt is that I don't know from ...
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2answers
36 views

Image multi class classifier CNN

I have a problem, im designing a multiclass classifier to classify medic images, I have to classify in which grade of desease is it, this are 6 grades , each time the joint deforms a little, so, mi ...
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4answers
827 views

Understanding how convolutional layers work

After working with a CNN using Keras and the Mnist dataset for the well-know hand written digit recognition problem, I came up with some questions about how the convolutional layer work. I can ...
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How to change voice features in python without affecting speech/language features?

I am trying to build a CNN model which should be able to identify the language being spoken in an audio file. I have extracted the MFCC matrix (for 13 coefficients) for each audio file and trained it....
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Majority of feature maps of CNN are black

Assuming we have a following CNN : Conv->MaxPool->Conv->Maxpool->Linear. What does it mean - intuitively - if the majority of the feature maps of the first convolutional layer are black i....
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1answer
136 views

Tensorflow Image Classifier error while fitting [closed]

I was building a Image Classifier with tensorflow but i got stuck while fitting the model can somebody help me out. ...
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23 views

CNN is not learning anything

I'm training a CNN network to detect relations between entities in written texts. I am suffering from an overfitting problem, I have high accuracy and low loss at the training step, but my model can't ...
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1answer
33 views

For semantic sementation, why am I getting better loss values with binary cross entropy than dice coef?

I'm learning all related to data science and how to train U-Net to do semantic segmentation. I have a U-NET with this loss function: ...
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10 views

Self-learning NN that finds unusual patterns in pictures

For a defect detection project, I'm trying to find a way for a neural net to be able to detect unusual patterns in images. Most of the images are very identical (95-99% of them) and sometimes a defect ...
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Getting error while performing late fusion of audio and video feature vectors. Please help me resolve this

I am trying to build Multimodal emotion classifier for which I have created CNN based Audio and Video models separately. Below are the implemented CNNs: ...

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