Questions tagged [convnet]

For questions regarding "Convolutional Neural Networks" (CNN)

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Why can't SPPnet update convolutional weights according to the fast R-CNN paper?

While discussing the drawbacks of SPPnet in the fast R-CNN paper, the authors state "But unlike R-CNN, the fine-tuning algorithm proposed in [SPPnet] cannot update the convolutional layers that ...
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2answers
368 views

How to determine the number of the training images in Keras after data augmentaion?

I want to create a CNN model and I am using data augmentation. I want know the number of augmented images in Keras. How to determine the number of the training images in Keras after data augmentation?...
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4k views

How to properly save and load an intermediate model in Keras?

I'm working with a model that involves 3 stages of 'nesting' of models in Keras. Conceptually the first is a transfer learning CNN model, for example MobileNetV2. (Model 1) This is then wrapped by a ...
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2answers
130 views

What is preferred upsampling or zero padding?

When training a CNN one option is either to zero pad an image to make it bigger or upsample it. When should I choose each one? What criteria is leveraged for choosing a method?
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How does deep learning helps in detecting multiple objects in single image?

Let's say there are two cars in an image. How can it detect these cars, given that it can detect single car in an image?
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1answer
49 views

Is there any work done on reconfigurable convolutional neural networks?

Convolutional Neural networks are used in supervised learning meaning models are always "set in stone" after training (architecture and paramters) so this might not even be possible, but is there any ...
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1answer
17 views

How to Connect Convolutional layer to Fully Connected layer in Pytorch while Implementing SRGAN

I was implementing the SRGAN in PyTorch but while implementing the discriminator I was confused about how to add a fully connected layer of 1024 units after the final convolutional layer My input ...
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2answers
52 views

ConvNet with concatenated data

I have a basic question regarding convolutional neural network. Assume I have a set of 1000 RGB images and I train a CNN from this set. I can obviously split each of my RGB images into 3 different ...
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1answer
130 views

Doing a fine tuning after a transfer learning

I red about fine tuning and transfer learning for CNNs and I was wondering if we can do fine tuning after using transfer learning on the same CNN , if so will this increase the performance of the ...
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1answer
17 views

How to build a classification pipeline that will pass to another model?

Not sure if the title explained it, but I am trying to build a pipeline where it's like a decision tree, but also not. Say for example, I had a picture. The model classified the picture, but now I ...
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2answers
39 views

Do smaller neural nets always converge faster than larger ones?

In your experience, do smaller CNN models (fewer params) converge faster than larger models? I would think yes, naturally, because there are fewer parameters to optimize. However, I am training a a ...
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111 views

Tensorflow Conv3D with variable input size

I have a hypotethical question: Is it possible to train Conv3D with variable input size? Sample dim = Length x Width x Depth ; Depth are fixed per each samples, let's say 500. However Length x Width ...
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Theoretically Speaking, How Do Squeeze-and-Excitation Blocks Help?

A SE block works by assigning a weight to each channel, contrary to a vanilla filter, which gives equal importance to all channels. My question is, theoretically speaking, shouldn't a regular filter ...
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1answer
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Feature Extraction from Convolutional neural network (CNN) and using this feature to other classification algorithm

As in this, the author is using CNN to extract features of the images, and then doing SVM for further analysis. My question is how to extract features in CNN? E.g., here is a CNN code I'm using: ...
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About the relevance and interprertability of convolutional filters?

Convolution filters are known to perform very well in tasks, concerning some work with the image or video data, due to their ability to preserve some spatial information and equivariance property ...
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2answers
384 views

MLP conv layers

When should MLP conv layers be used instead of normal conv layers? Is there a consensus? Or is it the norm to try both and see which one performs better? I would love to better understand the ...
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1answer
419 views

Setting input shape for an NLP task in R(Rstudio) using keras 1D convolution layer, when it expects 3 dimensional input (a tensor)

I am using R programming language and using Keras API to build a functional 1D CNN. I have a matrix of my dataset of the following shape rows*features (6000*1024). The input layer is set using the ...
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2answers
558 views

Model not learning when using transfer learning

I am working on a personal project on image classification (two classes) and am trying to see how the MobileNet v2 structure would perform. While training the training accuracy is already quite high ...
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1answer
156 views

Why is convnet transfer learning taking so long?

I am using transfer learning to train a binary image classification model using keras' pretrained VGG16 model. The code can be found below : ...
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1answer
825 views

Keras bug NasNetlarge no top

I am trying to use NasNetlarge in Keras without the top but I cant get rid of the top: ...
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25 views

reHow to understand the network structure in this paper( a multiple timeseries fusion model )

Please don't mark this question as duplicated to Can I create a layer with multiple rnn cell ? [question about a paper] It has already been marked 2 times , I admit they do refer to a same paper, but ...
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74 views

Keras ValueError: Shapes (64,) and (32,) are incompatible

I am trying to run my first CNN model on the Fashion MNIST dataset. I am using kerastuner to tune the hyperparameters. The below code gave me an accuracy of 90.4% on test, 92.2% on validation and 94....
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1answer
166 views

Why is training and validation loss steadily rising (eventually to NaN) in this CNN of mine?

Dear ML and data scientists: I have 4 layers of gray scale images for every single biological specimen in my dataset. I am trying to train a 4-convolution CNN (see pytorch architecture below) to ...
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1answer
2k views

Gradient flow through concatenation operation

I need help in understanding the gradient flow through a concatenation operation. I'm implementing a network (mostly a CNN) which has a concatenation operation (in pytorch). The network is defined ...
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1answer
50 views

Are there any control-flow/conditional statements in AI/ML models?

I was recently asked this during an interview. When we write a C program, it has a control-flow in the form of conditional statements like if, ...
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1answer
599 views

1x1 convolutions, equivalence with fully connected layer

I'm confused by the concept of equating a 1x1 convolution with a fully connected layer. Take the following simple example of a 1x1 convolution of 2 input channels each of size 2x2, and a single output ...
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4answers
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Model Validation accuracy stuck at 0.65671 Keras

I am using conv1d to classify EEG signals, but my val_accuracy stuck at 0.65671. No matter what changes i do, it never go beyond 0.65671. Here is the architecture ...
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1answer
53 views

TensorFlow 2 one-hot encoding of labels

I was following this basic TensorFlow Image Classification problem, where images of flowers have to be classified into one of 5 possible classes. The labels in the training set are not one-hot encoded,...
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1answer
52 views

Varying Image sizes in Tensorflow Malaria dataset | Dealing with unclean tensorflow data

I am trying to build a CNN based image recognition system for the Tensorflow malaria dataset. I loaded the dataset (~27k RGB images) using conventional tensorflow_datasets syntax. After some data ...
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4answers
1k views

What is the state-of-the art ANN architecture for MNIST?

What is actually the best neural network architecture for the classic MNIST digit classifying task? I couldn't find any that would claim to be the winner...
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1answer
530 views

How to use PCA in CNN for image recognition using Keras?

I created a CNN model for image classification and I want to use Principal Component Analysis (PCA) but when I run pca.fit() code, the code still running for hours ...
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0answers
10 views

torch.save(the_model, PATH) vs torch.save(the_model.state_dict(), PATH) - model loading incorrectly for one method

I just now noticed that the model does not get loaded correctly if I use the the_model.state_dict() method to save it. On the other hand, using ...
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2answers
500 views

Keras Conv1D model Input_shape value error

I am not sure why I am receiving this value error. Additionally, I haven't found a tutorial that explicitly talks about the appropriateness of size of filters and kernel. I would appreciate some input ...
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1answer
63 views

How do I deal with additional input information other than images in a convolutional neural network?

I try to convert a game state of a board game into the input for a convolutional neural network. A convolutional neural network is useful because the players have to place items on the board, and the ...
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1answer
80 views

Reason for Training and test loss sudden increment after some epochs keras

We know that if training and test loss are different from each other, our model is over-fitting. However, if both get high after some epochs, how can we justify it? One way to solve it is to reduce ...
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9 views

Nan loss for one Image dataset but finite fraction loss for another dataset?

I am training neural network using chainer library on MNIST dataset and one other dataset. As MNIST dataset is greyscale, hence I converted the other colored dataset to greyscale using cv2 library. I ...
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1answer
45 views

What is the best way of combining audio and visual data to make predictions?

I am trying to predict the probability of a disease by using audio and images, the audio and the images do not come from the same source. I am thinking of combining the outputs (maybe average them) of ...
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0answers
44 views

How do you deal with variable input sizes with an encoder-decoder net with skip connections in Keras?

I am currently getting into image segmentation with Keras, and I am using an encoder-decoder type as in the image below. My problem is that applying a MaxPooling2D ...
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1answer
262 views

LeNet-5 - combining feature maps in C3 layer

Famous LeNet-5 architecture looks like this: The output of layer S2 has dimension: 10x10x6 - so basically an image with 6 convultions applied to it to derive features. If each dimension was again ...
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3answers
87 views

Problem with overfitting

I make small CNN from scratch to classify barcodes. I have two classes: one for images with barcodes and second for all what isn't barcodes (items, animals, landscape, furniture, people). I got good ...
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1answer
44 views

Where are the 60 million params of AlexNet?

On the abstract of the AlexNet paper, they claimed to have 60 million parameters: The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some ...
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3answers
3k views

How many parameters in a Conv2d Layer?

I was following andrew-ng coursera course on deep learning and there's a question that has been asked there which I couldn't figure out the answer for? Suppose your input is a 300 by 300 color (RGB) ...
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1answer
30 views

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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19 views

Training CNN for object detection

I have an idea for build object detection model and I would like to share it with community in order to see if it makes sense. Dataset: I've gathered images with different shapes and annotated them ...
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1answer
554 views

How to increase accuracy of model from tensorflow model zoo?

Situation: My dataset is 70k images of people wearing clothes. Images are labeled: bbox position and class. There are 10 classes. I did 80:20 split. Categories are balanced with exception of one ...
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10 views

Intuition behind CNN Training Accuracy being Different than Loaded Model predicted on same exact data

I am trying to display some metrics in my final evaluation of several CNN models that I have trained using Tensorflow/Keras. I want to list the training accuracy for demonstration sake. However, I am ...
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1answer
50 views

When do I use Multiply and Add

I want to know the effect of Add and Multiply in keras by functionality. The dumb way of thinking is that they are meant to add ...
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1answer
14 views

Unstable results in test mode with fractional max pooling in PyTorch

I make some variants of ResNet, originally found in TorchVision, modify them, train them and so on. What I have found is that even in .eval() mode, even if I load state right before evaluation, I ...
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83 views

Combining 2D Detection with Disparity Maps to Learn 3D Object Geometry

Since the disparity map above is a representation of the object's distance from the camera's origin, is it reasonable to assume that a network (perhaps a convolutional LSTM) could be trained to ...
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14k views

How to improve loss and avoid overfitting

I'm trying to build a 2 class image classifier using the architecture suggested in first part of this blog https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data....

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