Questions tagged [convolutional-neural-networks]

For questions about convolutional neural networks, also known as CNN or ConvNet.

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Is there a common way to build a neural network that seeks to extract spatial and temporal information simultaneously?

Is there a common way to build a neural network that seeks to extract spatial and temporal information simultaneously? Is there an agreed up protocol on how to extract this information? What ...
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Different predictions across DL Frameworks [closed]

Can anyone give me a reason as to why I can train a neural network in say Tensorflow Flow, build equivalent models in pytorch and keras and any other DL framework, load the weights from the tensorflow ...
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How to have closer validation loss and training loss in training a CNN

I am using an AlexNet architecture as my Convolutional Neural Network. A learning rate of 0.00007 and 128 batch_size. I have 20000 data and 10% test, 40% validation, and 50% for training. I used 100 ...
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1answer
78 views

When training a CNN, what are the hyperparameters to tune first?

I am training a convolutional neural network for object detection. Apart from the learning rate, what are the other hyperparameters that I should tune? And in what order of importance? Besides, I read ...
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1answer
38 views

Pytorch Deep q network not learning and step not stepping towards target

I am trying to create a simple deep q network for rl with conv2d layers. I can’t figure out what I am doing wrong, and the only thing I can see that doesn’t seem right is when I get the model ...
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1answer
49 views

When should we use separable convolution?

I was reading the "Deep Learning with Python" by François Chollet. He mentioned separable convolution as following This is equivalent to separating the learning of spatial features and the ...
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2answers
122 views

What is the need for so many filters in a CNN?

Consider the following coding line related to CNNS Conv2D(64, (3,3), strides=(2, 2), padding='same') It is a convolution layer with filter size $3 \times 3$ and ...
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1answer
28 views

How to construct input dependent convolutional filter?

I am constructing a convolutional variational autoencoder for images, starting out with mnist digits. Typically I would specify convolutional layers in the following way: ...
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1answer
43 views

How can I train a CNN to detect when a person is smoking outside of shop given images from a video camera?

My friend is working at a pizza shop. He takes cigarette breaks in an area that is covered by the public webcam of our town. I now want to train a convolutional neural network to be able to detect ...
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1answer
31 views

Best Camera and protocol for embedded real time CNN project

I'm looking to develop a stand-alone real-time outdoor imaging CNN application, and I can't wrap my head around the myriad of camera options and their communication protocols. The target is a Linux ...
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1answer
47 views

Is it possible to know the distance objects are from camera based on only knowing one object's height?

I am doing a project where I have to know distance a particular object is from camera. In the photo I only know one of the object's height, but I don't know how far away that object is and I don't ...
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5answers
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CNN for detecting not just the nature of the object, but position within image as well

Consider a typical convolutional neural network like this example that recognizes 10 different kinds of objects from the CIFAR-10 dataset: https://github.com/tflearn/tflearn/blob/master/examples/...
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Computer vision - Can you put more weight on a specific part of the object?

Let's say I'm looking for any item that has a certain shape (outline) in a photo. but I can further classify it only according to particular features, that most of them are expected to be shown only ...
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1answer
64 views

Semantic Segmentation For Multiple Objects When Trained On Single Object

More of a conceptual question here: I'm working on semantic segmentation tasks in the medical space using the U-Net. Let's say that I train a U-Net model on medical images with the goal of segmenting ...
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1answer
50 views

Are mult-adds and FLOPs equivalent?

I am comparing different CNN architectures for edge implementation. Some papers describing architectures refer to mult-adds, like the MobileNet V1 paper, where it is claimed that this net has 569M ...
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1answer
23 views

Image Classification for watermarks with poor results

Just starting learning things about tensorflow and NN. As an exercise I decided to create a dataset of images, watermarked and not, in order to binary classify these. First of all, the dataset ( you ...
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1answer
40 views

How deep convolution filters should be?

In ConvNet, after having decided the number of layers for it, one has to decide the filter size and depth for each layer. The intuition behind the spatial filter size is the number of pixels in the ...
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0answers
9 views

Cocatenate feature extractor layers with different channels

I have a network architecture for feature extraction and I wanted to concatenate layers of the same dimensions but with different feature channels. ...
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0answers
19 views

Choice of loss function for semantic segmentation

I am training a U-Net for semantic segmentation of large medical images (4096x4096px). The two classes are "too" unbalanced. The white pixels are just about 0.1% (or less) of the whole image....
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1answer
39 views

Comparing a large/general CNN to a smaller more specialized one?

I am still somewhat a novice in the ML world, but I had a strange idea about CNNs and wanted to ask if this would be a valid way to check the robustness of a general CNN that classifies certain images....
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1answer
201 views

Artifacts After pruning Unet CNN

Im trying to make a dark image brighter using CNN-UNet arcitecture. When I train the network I get the following results: When I cut the features in half for pruning, and do full train again, I get ...
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1answer
94 views

Should I use single or double view for gender recognition?

My project requires gender recognition of people shown on the given images, with more than one person per image. However, these people can be positioned in frontal or side view(passing by ...
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2answers
873 views

What to do if CNN cannot overfit a training set on adding dropout?

I have been trying to use CNN for a regression problem. I followed the standard recommendation of disabling dropout and overfitting a small training set prior to trying for generalization. With a 10 ...
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1answer
429 views

Game AI - Fast python OCR or cropped image input

I'm developing a Game AI which tries to master racing simulations. I already trained a CNN (alexnet) on ingame footage of me playing the game and the pressed keys as the target. As the CNN is only ...
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0answers
29 views

CNN validation decreased and then fluctuated forever

I'm working on learning CNN. The problem I encountered was to reduce the amount of validation loss to a number of epochs and then a lot of fluctuation around the error of 0.1. Then the error and ...
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0answers
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Pytorch and keras ddqn seem identical, only keras learns

I followed a tutorial for ddqn to beat pong, it beats it with a perfect score in keras, but trying to translate it to pytorch it doesn't learn at all. What am I missing? I pasted all the code for each ...
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1answer
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What is the dimension of my output of the form (2n + 1, 2n + 1, #filters) after a MaxPooling layer

I'm trying to white board the different mechanisms behind a convolutional neural network. I have on question regarding the dimension of my volume after using a max pooling layer. Let's suppose I have ...
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1answer
32 views

How to use 'Canny/Watershed' algorithm's output as an input for Image Classification Model

I have a very silly problem in hand. I have implemented 2 methods which give me the mask to separate the objects from the background. What I get from one method is the object encapsulated in the red ...
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1answer
35 views

Convolutional Sequence to Sequence Learning kernel parameters

I am reading the paper Convolutional Sequence to Sequence Learning by Facebook AI researchers and having trouble to understand how the dimensions of convolutional filters work here. Please take a look ...
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0answers
15 views

Model output segmentation maps which are not full

I created a VGG based U-Net in order to perform image segmentation task on yeast cells images obtained by a microscope. There are a couple of problems with the data: There is inhomogeneity in the ...
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0answers
24 views

Training a CNN for semantic segmentation of large 4600x4600px images

I am trying to implement a CNN (U-Net) for semantic segmentation of similar large grayscale ~4600x4600px medical images. The area I want to segment is the empty space (gap) between a round object in ...
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2answers
4k views

How fast is a GPU compared to a CPU for training deep learning models, and which GPU should I buy?

First of all, I don't play games at all, and I am still quite new to deep learning. I was using AlexNet (transfer learning, actually) in MATLAB to classify images in my current laptop (i5-3230, ...
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2answers
109 views

Which neuron represents which part of the input?

In a neural network, each neuron represents some part of the input. For example, in the case of a MNIST digit, consider the stem of the number 9. Each neuron in the NN represents some part of this ...
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2answers
72 views

Backpropagation of neural nets with shared weight

I am trying to understand the mathematics behind the forward and backward propagation of neural nets. To make myself more comfortable, I am testing myself with an arbitrarily chosen neural network. ...
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1answer
97 views

What should load_mask() return if an image doesn't have any objects? (Mask RCNN)

I want to use Mask RCNN to do image segmentation. I need to override the load_mask function for the dataset class. I know this function should return mask tensors and class ids of objects in an image. ...
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1answer
45 views

Is it a good idea to overfit on a small part of your data for faster model convergence?

I working on a classification problem that needs to detect patterns on a time serie. Basically, there's a catch-all class that means "no pattern detected", the other are for the specific patterns. The ...
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4answers
141 views

If we want to classify something as either a cat/dog or neither, do we need 2 or 3 classes?

Suppose one trains a CNN to determine if something was either a cat/dog or neither (2 classes), would it be a good idea to assign all cats and dogs to one class and everything else to another? Or ...
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2answers
39 views

Stride in convolutional neural networks (horizontal/vertical direction)?

I feel like this question has been asked before, but I can't seem to find any old material on this. In the convolutional layer for CNNs, when you specify the stride of a filter, typical notes show ...
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1answer
54 views

not sure if fine-tuned network is finely-tuned

I am practicing with Resnet50 fine tuning for binary classification task, here is my code snippet. ...
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1answer
53 views

How can I sample the output distribution multiple times when pruning the filters with reinforcement learning?

I was reading the paper Learning to Prune Filters in Convolutional Neural Networks, which is about pruning the CNN filters using reinforcement learning (policy gradient). The paper says that the input ...
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2answers
35 views

Heavily mixing signal differentiation from Open Set of backgrounds via CNN

To whomever can help out, I appreciate it. I am currently attempting to detect a signal from background noise. The signal is pretty well known but the background has a lotttt of variability. I've ...
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28 views

How to implement or avoid masking for transformer?

When it comes to using Transformers for image captioning is there any reason to use masking? I currently have a resnet101 encoder and am trying to use the features as the input for a transformer model ...
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5answers
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Can I build a CNN for image classification tasks just with OpenCV? [closed]

I have practiced building CNNs for image classification with TensorFlow, which is a nice library with good documentation and tutorials. However, I found that TensorFlow is too complicated and ...
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1answer
56 views

Can we apply transfer learning between any two different CNN architectures?

There are many types of CNN architectures: LeNet, AlexNet, VGG, GoogLeNet, ResNet, etc. Can we apply transfer learning between any two different CNN architectures? For instance, can we apply transfer ...
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1answer
217 views

What are the ways to calculate the error rate of a deep Convolutional Neural Network, when the network produces different results using the same data?

I am new to the object recognition community. Here I am asking about the broadly accepted ways to calculate the error rate of a deep CNN when the network produces different results using the same data....
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1answer
236 views

How can I incrementally train a Yolo model without catastrophic forgetting?

I have successfully trained a Yolo model to recognize k classes. Now I want to train by adding k+1 class to the pre-trained weights (k classes) without forgetting previous k classes. Ideally, I want ...
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1answer
37 views

Should I remove the text overlaying some images in the dataset before training the CNN?

If I am attempting to train a CNN on some image data to perform image classification, but some of the images have pieces of text overlaying them (for the purpose of description to humans), then is it ...
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1answer
54 views

Machine learning to find coordinate in image

I am trying to figure out how to approach this. Given training data of images and the pixel coordinates of the centre of an object in that image, would it be possible to predict the pixel coordinates ...
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1answer
36 views

Research paths/areas for improving the performance of CNNs when faced with limited data

I've been reading through the research literature for image processing, computer vision, and convolutional neural networks. For image classification and object recognition, I know that convolutional ...
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0answers
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Duplicating calculations in CNN-LSTM architecture

I want to use frames from video game and analyze them using CNN and LSTM. But when I have the model defined like that ...

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