Questions tagged [vgg16]
Use for questions about VGG16, the Convolutional Neural Network (CNN) model developed by researchers at the University of Oxford.
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extract features from low resolution
I have medical images and need to extract features from the layer before the classification layer using VGG for example but the ...
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Using large CNNs (e.g., ResNet) in convolutional autoencoders for image representation learning
I am confused about which CNNs are generally used inside autoencoder architectures for learning image representations. Is it more common to use a large existing network like ResNet or VGG, or do most ...
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When using a model like VGG16 as a classifier within Faster RCNN, does Faster RCNN then use 2 CNNs in total?
Im currently doing a project about CNN's but im quite confused because they can be used to classify and to extract features. According to the Faster RCNN paper, it uses a ResNet backbone. I have also ...
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VGG16 and LSTM Model Performance Issues
My model has been performing poor recently and I was wondering what are some things I can do bolster its performance. So far its training ACC is low, and validation is constant and not improving
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What is the difference between features in vgg
I read the architecture of the model but this is the first time I try to use it . The calculations of the features map will be different if I extract the features from the two last layers or from the ...
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I am looking for general image-based clustering methods
My task is to cluster some images, I decided to use the VGG model to extract the features and then use K-Means to cluster these features.
But my question: When I use a VGG as a feature extractor, I ...
user119783
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35
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What is num_groups in GroupNorm and how to choose it
I found that batch_norm can cause problems with small batch sizes and that GroupNorm is a good alternative. Now, GroupNorm requires two parameters, the num_group and the num_channels. How can I choose ...
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VGG16 (pytorch) training issues using a very large dataset vs a smaller dataset
I've constructed a simple VGG16 layer model from the original Simonyan & Zisserman paper for use on a DBT (Digital Breast Tomo.) data challenge. As a starter model, I chose to make the 3x244x244 ...
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57
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Shared classifier for 3 neural networks (is this weights sharing?)
I would like to create 3 different VGGs with a shared classifier. Basically, each of these architectures has only the convolutions, and then I combine all the nets, with a classifier.
For a better ...
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2
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How to get >=85% accuracy on 3-class classification task
Now I am solving the problem of 3-class classification (in the task you need to understand who is in the picture - a panda, a cat or a dog). The dataset consists of 3000 images. To solve the problem, ...
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What should the output sizing be for a class that returns multiple image arrays for a dataloader
I have a custom image class (mainly borrowed from examples) that takes in an image of size 3x244x244 for use in a VGG model and returns augmented versions (rotations of 90,180,270 and an Hflip). ...
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Is there wights of voice or audio for VGG or Inception?
I want to use VGG16 (or VGG19) for voice clustering task.
I read some articles which suggest to use ...
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Using vgg16 or inception with wights equals to None
When using pre-trained models like vgg16 or inception,
it seems that one of the benfits of using pre-trained model, is to save ...
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Plot a training/validation curve in Pytorch Training [closed]
I have the following training method and I'm confused how may I modify the code to plot a training and validation curve history graph with matplotlib
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How Does EAST detector implementation with VGG16 look? How many outputs does it have?
I was reading the Efficient and Accurate Scene Text Detector paper and saw the author reference VGG-16 as a possible stem "feature extractor" network. In the paper they say:
In our ...
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What is Deep learning approach to count the number of Diamonds in an image?
I am working on a project which involves counting the number of diamonds in the provided image. I have a set of images and a VIA annotated .json file which has all the annotations. How do I proceed ...
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Error after merging two Deep Learning models VGG16 and ResNet50
I have merged two different models namely VGG16 and ResNet50 and given the outputs of the two models as input to another model. I have checked the Layers graph is correct. Before merging the code was ...
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Training Accuracy is getting higher, but Valid Loss and Accuracy is same every epoch
I'm doing a transfer learning with ResNet50. My dataset
is clothes(224x224x3), and
49 category(classes) -> training data 1000 per 1 category,
total 49000. And
valid data 200 per 1 category, total ...
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2
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251
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Semantic segmentation with greyscale images
I'm trying to reproduce a research with greyscale images instead of colour images.
I have found that there are pre-trained networks, like VGG16, with ImageNet. But that dataset has colour images, and ...
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class activation mapping when accuracy is 100%
I am a beginner to image classification and apologies beforehand if the question I am asking is dumb. I am currently using the following model:
...
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Why the validation accuracy does not increase in a normal way over the epochs?
I'm trying to transfer learning VGG16 model with imagenet in a dataset of retinal images but i'm confused to get a graph like this in the picture below, I don't know why the validation accuracy didn't ...
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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 ...
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2
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Why is VGG16 training accuracy is constant?
I want to train a model using VGG16 to classify radio signals by their modulation typ. similar to this paper (Over the Air Deep LearningBased Radio Signal Classification) So I have built the model ...
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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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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
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467
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CNN implementation low accuracy on MINST data
I'm trying to implement VGG11 (Model A of Table 1 from this article) on the MINST dataset but I'm getting ~10% train & test accuracy (as bad as random guessing). I had to resize the MINST data ...
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How can I increase my accuracy avoiding overfitting? CNN-Keras-VGG16
As I asked in this question: Why are my predictions bad, if my accuracy in train is roughly 100% (Keras CNN) , my problem was Overfitting, so, I reduce the number of layers, and now I have this model:
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user84391
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How to fine tuning VGG16 with my own layers
I want to maintain the first 4 layers of vgg 16 and add the last layer. I have this example:
...
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1
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How to find which patch in orignal image does an activation correspond to in vgg net after the final pooling layer
So I am working on the NeurIPS 2019 reproducibility challenge, The link to the paper is https://arxiv.org/abs/1806.10574. So basically we have a vgg-16 net with the final fully-connected layers ...
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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:
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user84391
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Can I save only some VGG19's layers into a .H5 file?
I am training a deep-learning style transfer model with the pretrained-VGG19 CNN.
My aim is to use it in my Android app for personal purposes with Google Firebase Machine Learning Kit (which would ...
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1
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How to increase the accuracy of my predictions (CNN fine tuning VGG16 KERAS)
In my VGG16 fine-tuning, I have to classify retinal images in 2 classes (or 4th stage or not 4th stage) and I have 700 images each class to train. This is my code now:
...
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vgg16 needs less epochs than resnet ,why?
Recently i Have been comparing the vgg16 with resnetv1 with 20 layers.I have found out that although each epoch on vgg takes more time to complete,it generally needs less epoch to reach a certain ...
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Transfer learning VGGish (AudioSet). Impact of zero padding to fit the input size
I am trying to train a network on top of the VGGish architecture (https://github.com/tensorflow/models/tree/master/research/audioset/vggish), using (transfer learning) finetuning.
I initially started ...
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293
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How to find the class name of a new image from the pre-trained model
I would just like to get the class names of the predictions. I can get the class names on the images that I trained the model. But if I predict an image (say which is not trained but already belongs ...
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Is Faster RCNN the same thing as VGG-16, RESNET-50, etc... or not?
My understanding is that Faster RCNN is an architecture for performing object detection. It finds objects in an image and classifies them. My understanding is also that VGG-16, RESNET-50, etc... also ...
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Key pixels, key "features" detection in CNNs
I am working on a dataset but I don't know what the labels mean.
I was wondering if using CNNs there was a way to understand which pixels where most significant for the network.
A little bit in the ...
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Improve accuracy of Keras multiclass image classification with pretrained VGG16 conv_base
In the moment, I'm training my first "larger" image classification model with Keras (22 classes, 2000 train samples, 500 val samples each class). I use a pretrained model (VGG16). My current model is ...
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Neural Network Model using Transfer Learning not learning
I am a beginner in Deep Learning and working on Road Crack detection using transfer learning. I am working on binary classification with two classes , crack and no crack.
My distribution of two ...
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Using the first 3 layers of a pretrained network in Keras
I want to use the 3rd layer's output of the VGG16 network. The error is like below:
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How to calculate $\phi_{i,j}$ in VGG19 network?
In the paper Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network by Christian Ledig et al., the distance between images (used in the loss function) is calculated from ...
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How to save prediction values for the whole data in Keras
I am using pre-trained VGG16 model to classify images located in the folder. Currently, I am able to classify only one single image.
How can I modify the code to classify all the images in the ...
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Image features (produced by VGG19) do not properly train an ANN in Keras
I've used a VGG16 network to extract features from an image dataset, creating a features dataset using the following code:
...
user42078
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Overfitting in CNN
I am training a VGG net on STL-10 dataset
I am getting Top-5 validation accuracy about 98% and Top-1 validation accuracy about 83%
But both the Top-1 and Top-5 Training accuracy is reaching 100%
...
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870
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Is this an over-fitting case?
I'm a new programmer and this is my first ever neural network for real world application.
Here is the deal, I'm using a top-less pre-trained VGG-16 with some dense layers on top of it.(for image ...
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Why is input preprocessing in VGG16 in Keras not 1/255.0
I am just trying to use pre-trained vgg16 to make prediction in Keras like this.
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