Questions tagged [convnet]

For questions regarding "Convolutional Neural Networks" (CNN)

Filter by
Sorted by
Tagged with
0
votes
0answers
15 views

Configuration of CNN model for recognition of sequential data - Architecture of the top of the CNN - Parallel Layers

I am trying to configure a network for character recognition of sequential data like license plates. Now I would like to use the architecture which is noted in Table 3 in Deep Automatic Licence Plate ...
0
votes
0answers
24 views

How to deal with severe overfitting in a UNet Encoder/Decoder CNN in a task very similar to image translation?

I am trying to fit a UNet CNN to a task very similar to image to image translation. The input to the network is a binary matrix of size (64,256) and the output is of size (64,32). The columns ...
0
votes
1answer
13 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 ...
0
votes
0answers
7 views

Local RoI features in Faster/Mask R-CNN

I use Faster and Mask R-CNN for various problems, including training GANs. I'm particular interested in local features that Region Of Interest module in Faster R-CNN extracts from feature layer(s). ...
0
votes
0answers
6 views

Segmentation-free character recognition on an image: Multi-label, multi-class or sequential image classification problem?

I have some images which look like this one: They exist of 3 possible characters (A-C) and a length of 4. Now, I would like to run a neural network, which recognizes each character in the picture ...
1
vote
1answer
19 views

CNN model contains several images that are null

I'm using a deep CNN with the ReLU activation function. When visualizing the layers (each with 32 filters), several of the filtered images are zeros. I am trying to reason why this may be happening? ...
0
votes
1answer
36 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 ...
0
votes
0answers
14 views

Definitive Values in Confusion Matrix

I built a convolutional LSTM model for the classification of 4-image time series. I used n keras ConvLSTM layers, followed by a time-distributed flatten and a few dense layers, finalized by a dense ...
5
votes
3answers
132 views

Disparity between training and testing errors with deep learning: the bias-variance tradeoff and model selection

I am developing a convolutional neural network and have a dataset with 13,000 datapoints that is split 80%/10%/10% train/validation/test. In tuning the model architecture, I found the following, after ...
0
votes
0answers
7 views

Common sub-networks

I am pruning a neural network (CNN and Dense) and for different sparsity levels, I have different sub-networks. Say for sparsity levels of 20%, 40%, 60% and 80%, I have 4 different sub-networks. Now, ...
1
vote
0answers
23 views

How to interpret Deep learning network architecture into a diagram?

How to draw this Deep learning network architecture diagrams? I'm using Faster R-CNN: R50-FPN. Any ideas or tip to convert this to a diagram? Or just to know which are input, hidden and output ...
0
votes
0answers
10 views

AI architecture for time and spacial sequences

I am working on a project where I analyse MEG data. I have 102 channels as a vector and a 2D matrix of the channels (11x14) to show spatial relations - I want to include that in the AI architecture. ...
1
vote
0answers
47 views

Gender Prediction from Offline Handwriting Using Convolutional Neural Networks

Starting from the fact that handwritten documents style are gender-dependent (male and female have different writing styles), I'm trying to predict writer's gender from its handwritten scripts using ...
0
votes
0answers
10 views

Annotating images for CNN; how to label partially obscured images

I am annotating images to train a CNN classifier, some images are partially obscured, generally speaking what is the intuition and advice in these situations, should a partially obscured image be ...
0
votes
1answer
9 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 ...
0
votes
0answers
10 views

How to download and see the actual model-creation script for the ResNEXT model at this github?

I'm new to using python/scripting and AI. I was looking into https://github.com/facebookresearch/semi-supervised-ImageNet1K-models that describes ResNEXT (variant of Resnet). Clicking on ...
1
vote
0answers
11 views

Using a 3-D convolutional layer to simulate a 2-D convolutional layer

I've asked this question on the AI StackExchange , but I received no insight so I'd like to ask it here. Is using a filter of size (1, x, y) on a 3-D convolutional layer the functionally equivalent ...
2
votes
0answers
10 views

What is the meaning of 'concatenate' in this neural network architecture?

I am trying to understand the lane edge proposal network proposed in LaneNet for lane detection. My understanding of this is that a number of convolutional and pooling layers are first used to ...
1
vote
0answers
12 views

The idea behind Generalized Max Pooling

I am trying to understand the idea of "Generalized Max Pooling". It seems they try to make the 'pooled' representation similar to the features. If so I feel some rare discriminating features could ...
0
votes
0answers
7 views

Best model structure for input coordinates producing full images

I am trying to produce a machine learning model that works as an interpolator of real data. Essentially what would go into the model is an xy coordinate the result ...
0
votes
0answers
19 views

Free dataset to train a neural network in order to extract text from image

I'm building a custom OCR to recognize and get text from png image. In order to dealing this task, i'm using python with tensorflow library (1.14.0) to develop a Convolutional Neural Network that ...
0
votes
0answers
36 views

Imblanced-data: Need assistance with SMOTE technique for a CNN input

I am trying to apply the SMOTE sampling technique to over-sample the minority class of a multiclass (5-class) problem using the convolutional neural network. As far CNN requirement, the input shape ...
0
votes
0answers
89 views

Keras CNN model gives no gradients error during training

I’m trying to create a Convolutional Neural Network model, using an 824 image dataset, for predicting an output value. Problem is that the dataset is quite unstructured, as there are plenty of RGB and ...
1
vote
0answers
19 views

Preparing text for modeling in dialogue structure

I'm working on implementing the DialogueGCN code from this paper. Its a model that classifies the 'emotion' from utterances of text within a conversation. As this model takes into account speaker ...
0
votes
0answers
37 views

What does “full connection table” mean in Yan LeCuns comment on 1x1 convolutions?

What does "full connection table" mean in Yan LeCuns comment on 1x1 convolutions? In Convolutional Nets, there is no such thing as "fully-connected layers". There are only convolution layers with ...
0
votes
1answer
27 views

UNet Model accuracy is stuck at exact 0.5 (neither more or less) (No class imbalance, tried tuning learning rate)

This is using PyTorch I have been trying to implement UNet model on my images, however, my model accuracy is always exact 0.5. Loss does decrease. I have also checked for class imbalance. I have ...
0
votes
0answers
25 views

Dealing with low variation data

So my current project involves using a neural network to try and predict the probability of a player getting a kill in a first-person shooter. I've recorded a number of features that should be ...
3
votes
0answers
69 views

What happens with activations?

I am playing with convolution network, assembling something between AlexNet and ResNet. Not very deep, about 10 conv. layers including 2 through residual connection, and 3 fully-connected layes at the ...
2
votes
2answers
31 views

What can we understand from max-activation generated images?

There are several approaches to generate psychedelic images, providing maximum activations for individual neirons in convolutional neural networks. For example there is a lot of them there https://app....
2
votes
1answer
52 views

How to combine different models in Keras?

I have a pre-trained network, consist of two parts, the feature extraction, and the similarity learning. The network takes two inputs and predicts the images are same or not. The feature extraction ...
3
votes
0answers
97 views

How to detect vanishing and exploding gradients with Tensorboard?

I have two "sub-questions" 1) How can I detect vanishing or exploding gradients with Tensorboard, given the fact that currently write_grads=True is deprecated in the Tensorboard callback as per "un-...
1
vote
1answer
21 views

Finding the appropriate CNN Model Architecture and Parameters

I am currently creating a CNN model that classifies whether the font is Arial, Verdana, ...
0
votes
0answers
30 views

Is it possible to train YOLO (or other object detectors) from the raw architecture, rather than cloning\downloading it?

I am trying to do object detection but I am faced with some issues. I cannot install YOLO, or any other existing models that require cloning a repo, and a certain number of other limitations. ...
2
votes
0answers
17 views

Precision Recall using Distance Matrix

Given a Pre-trained CNN model, I extract feature vectors for 3450 Reference (Winter) and 3450 Query images (Spring) and compare features with euclidean distance to plot the distance matrix besides ...
2
votes
1answer
59 views

EfficientNet: Compound scaling method intuition

I was reading the paper EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks and couldn't get my head around this sentence: Intuitively, the compound scaling method makes sense ...
0
votes
0answers
63 views

Fusing batch normalization with deconvolution in neural networks

I am trying to raise the performance of my convolutional neural network and for that reason I am trying to implement batch normalization fusing. Things are fine when I use fuse with convolution layer,...
0
votes
0answers
10 views

What is the current state-of-the-art, out-of-the-box alternative to Darknet YOLO?

I am unfortunately unable to use YOLO. I am trying to implement another solution. Ideally, this would be a neural network architecture with weights that I could import easily like any other Keras/...
0
votes
0answers
20 views

What does “context” mean in the context of computer vision models

I'm reading this research paper and I do not understand what they mean by "high level context".
0
votes
0answers
12 views

Can one hardcode convolutional filters to detect characters in a CNN?

In Pytorch, you can hardcode your filters to be whatever you like. At the moment, I'm doing text detection and I need to identify the location of a certain information. This information always ...
0
votes
0answers
23 views

In what form the optical flow data is fed to a 3d cnn model?

I want to create a 2 stream architecture for video classification using keras and tensorflow as its back-end .In this method you basically give 2 types of data to the model.One is the video itself(...
2
votes
0answers
20 views

Why does this implementation of SimpleNet use 3x3 kernels on it's final layer for cifar10?

Question; I'm trying to implement simplenet in tensorflow and I have a question that I can't seem to answer myself. The implementation I'm basing this off of is here: https://github.com/Coderx7/...
3
votes
1answer
156 views

What do positive and negative gradient values mean for Convolutional Neural Network?

As we have the typicall pass of the neural network we make a forawrd pass to predict classes and then we have cost function and based on that we calculate gradients. I'm wondering what are the ...
2
votes
0answers
37 views

Understanding the significance of LeNet-5 w/ MNIST data set

I'm beginning to learn about conv nets and started with what I understand to be one of the seminal works: LeNet-5. However, my limited experimentation doesn't seem to show any advantage over a single ...
0
votes
1answer
46 views

How neural style transfer work in pytorch?

I am using this pytorch script to learn and understand neural style transfer. I understood most part of the code but having some hard time understanding some parts of the code. In ...
2
votes
0answers
69 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 : ...
2
votes
1answer
29 views

How do CNNs find different feature maps?

Assume I have a CNN that in the first (conv) layer takes a 1-channel signal (the input) and gives a 2-channel output. Let's further assume that the rest of the net has symmetric architecture from the ...
1
vote
0answers
13 views

Train a competitive layer on nonnormalized vectors using LVQ technique

How can we train a competitive layer on non-normalized vectors using LVQ technique ? The net input expression for LVQ networks calculates the distance between the input and each weight vector ...
0
votes
2answers
30 views

How to extract crucial features to create an image

Imagine, you have a dataset containing pictures of (example only, just to explain the task) cats and dogs. The data set is labeled, so we can train using supervised learning algorithms. My goal is to ...
1
vote
1answer
43 views

Which combination of 3 hyperparameters to combat overfitting of a convolutional neural network?

I have a small dataset with which I want to train a CNN by using Data Augmentation. Since the CNN is overfitting due to the small data set, I would like to optimize some hyperparameters. However, ...
0
votes
1answer
66 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 ...

1
2 3 4 5
10