Questions tagged [convnet]

For questions regarding "Convolutional Neural Networks" (CNN)

Filter by
Sorted by
Tagged with
0
votes
0answers
14 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
16 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
30 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
14 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
15 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
17 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
65 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
1answer
27 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
32 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 ...
2
votes
0answers
33 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
18 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
24 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
13 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
37 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
30 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
9 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
18 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
19 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
18 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
73 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
25 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
40 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
25 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
28 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
10 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
49 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 ...
0
votes
0answers
10 views

Modifying network to handle images consistently

I'm modifying IRNet to work with the Cityscapes dataset. This network takes images as input and is supposed to output images that can be used as instance segmentation labels. IRNet originally uses ...
0
votes
1answer
49 views

Improve performances of a convolutional neural network

I am doing image classificaition, and to do this I have built the following neural network: ...
1
vote
1answer
19 views

Validity of PU learning while using character-level encoding using CNNs for classifying text data

I'm trying to classify a large set of documents (~100M) as valid or invalid, based upon a small given set of labeled valid documents (~3k). I'd like to know if the PU learning approach described in ...
2
votes
1answer
136 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 ...
0
votes
0answers
6 views

if two convolution layer connected in tandem follow associative property of convolution?

Two Convolution filter follow the associative property as follows :- I want to ask whether this property will hold for two convolution layer with no operation in between them?
1
vote
1answer
32 views

To calculate my confusion matrix with recall and precision, my test set need to be equal(balanced)?

In my CNN, I have 200 'negative' images and 50 'positive' images in my test set and I want to make a confusion matrix. My doubt is if I have to equalize the samples in the dataset because if I keep ...
0
votes
0answers
111 views

Python Keras model performs much worse than R Keras model

I got this R Keras model from GitHub that performs really well. GitHub repo ...
1
vote
1answer
69 views

Why/When should I use VGG16 to do fine-tuning? [closed]

Why or When should I use VGG16 in my cnn? what is the pros and cons to use this model? I search but not found this answer. If you have references, I appreciate
2
votes
3answers
899 views

How to combine GridSearchCV with Early Stopping?

I'm a beginner in machine learning and want to train a CNN (for image recognition) with optimized hyperparameter like dropout rate, learning rate and number of epochs. The optimal hyperparameter I ...
1
vote
0answers
32 views

Why when I apply GaussianBlur in my images, my model overfit? CNN KERAS

I have 1400 images (700 each class), and i'm using vgg-16 to classificate between one and other class. But when I apply the preprocess method Gaussian Blur (which seem to be very much clear to see the ...
0
votes
0answers
51 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 ...
2
votes
0answers
30 views

Did I do the right thing in my CNN Keras (class imbalance - augmentation)

To implement my Binary CNN in keras, I had a dataset of ~~35000 images but only 700 is from one class and all the others are from the other class, so what I did: I get the 700 unique images from class ...
0
votes
0answers
27 views

How do I augment data after spliting traininng datset into train and validation set for CIFAR10 using PyTorch?

When classifying the CIFAR10 in PyTorch, there are normally 50,000 training samples and 10,000 testing samples. However, if I need to create a validation set, I can do it by splitting the training set ...
0
votes
1answer
37 views

What is a latent space vector?

I do not understand this about GANs. Apparently the Generator is supposed to receive a latent space vector as its input. Yet I couldn't find an example of how I can implement it in Pytorch. This is a ...
0
votes
0answers
22 views

CNN model with transfer learning not performing, training loss is still high, test accuracy is very low

Hi I'm trying to train a cnn model with transfer learning, and I am not able to get a good test accuracy (14%) - I don't know why it doesn't work for me. ...
4
votes
2answers
145 views

Why are my predictions bad, if my accuracy in train is roughly 100% (Keras CNN)

In my CNN i have to handle 2 classes in a binary system, I have 700 images each class to train, and others to validation. This is my train.py: ...
1
vote
0answers
16 views

What is the difference between these 2 training scenarios?

I have a very large dataset and due to computational constraints, I have to divide the data into 20 parts (each part is around 1.5GB). I constructed a deep CNN model using Keras for this dataset. The ...
0
votes
0answers
16 views

Process with which ImageDataGenerator with flow_from_directory() method fetches and augments images from a directory in Keras 2

When using Keras 2.0 ImageDataGenerator and in particular the flow_from_directory() method to perform data augmentation, how many images are generated and in what order? Do we have a way to control ...
1
vote
0answers
20 views

Shuffling the images with DataImageGenerator and flow_from_directory in a Medical application (tensorflow 2/Keras)

I am facing the following situation: I have CT images (scans) of patients where 10 images describe each patient. These images are stored in separate directories based on what is the focus of each ...
0
votes
0answers
49 views

ResNet50 Model is not learning with transfer learning in keras

I am trying to perform transfer learning on ResNet50 model pretrained on Imagenet weights for PASCAL VOC 2012 dataset. As it is a multi label dataset, I am using ...

1
2 3 4 5
9