7
votes
Accepted
Why are my predictions bad, if my accuracy in train is roughly 100% (Keras CNN)
This phenomenon is called overfitting. In short it means that your CNN has memorized the dataset, achieving $100\%$ training accuracy. This knowledge, however, doesn't generalize well to unseen data.
...
- 7,978
5
votes
Is Faster RCNN the same thing as VGG-16, RESNET-50, etc... or not?
Image Classification Models are commonly referred as a combination of feature extraction and classification sub-modules. Where the total model excluding last layer is called feature extractor, and the ...
- 151
5
votes
vgg16 needs less epochs than resnet ,why?
For some reason VGG might be better suited for cifar10 (maybe kernel sizes etc.). Generally speaking, however, this isn't the case. I've trained VGGs much slower than even the largest resnets (i.e. ...
- 362
3
votes
How to combine different models in Keras?
You can get the output of your models with model.output or get_layer
and combine them with ...
- 352
2
votes
How to understand conv layer to another same conv layer in VGG16?
to address your question about:
It is not clear that why Conv1(3)->Conv1(3)->Pool1(2) is better than Conv1(3)->Pool1
..lot of neural network design is a trial and error process and more art than ...
- 154
2
votes
Accepted
Is this an over-fitting case?
Your model is indeed overfitting. There can be a lot of reasons why it could be happening - How many images are you training with?, Aren't you using Regularization, data augmentation using random ...
- 115
2
votes
Why is input preprocessing in VGG16 in Keras not 1/255.0
The pre-trained weights that are available on Keras are trained with the preprocessing steps defined in preprocess_input() function that is made available for each ...
2
votes
Neural Network Model using Transfer Learning not learning
Transfer learning is done by chopping off the last layer in the pre-trained network (in your case it is VGG16) and add a dense layer depending on the number of classes you need and then train the new ...
- 21
2
votes
Neural Network Model using Transfer Learning not learning
Just take 2 images from your training data. One from class 'crack', another one from class 'not crack'. Now, check if your model can get a training accuracy of 100% which means it can overfit on the ...
- 71
2
votes
Accepted
How to calculate $\phi_{i,j}$ in VGG19 network?
In section 2.2.1 of the paper, they state that they use euclidean distance. I'm going to take your word that there are 512 filter activations in that layer; if I'm reading this right, there aren't ...
- 1,284
2
votes
vgg16 needs less epochs than resnet ,why?
I'm a pretty new to deep learning but will try to give an answer.
A short answer could be the number of features the VGG has compared to the resnet.
That being said, only relevant features are ...
- 151
2
votes
Accepted
Why/When should I use VGG16 to do fine-tuning?
I think you mixed up the terms that you do here. The task is called transfer learning and you FINETUNE the model.
The reasoning is that after experimentation we found that earlylayers of CNN captures ...
- 1,123
2
votes
Accepted
How to increase the accuracy of my predictions (CNN fine tuning VGG16 KERAS)
I have the following suggestions.
Make sure your preprocess function in predict.py is doing exactly what datagen is doing ...
- 173
1
vote
Accepted
Hello guys, is dimension reduction required for tensorflow?
Dimensionality reduction is not related to TensorFlow's CNN training:
Dimensionality reduction is for unsupervised data clustering and classification. Not sure if you will cluster expressions clearly ...
- 4,684
1
vote
How to get >=85% accuracy on 3-class classification task
The problem is dealing with multi-class classification. So, in output layer try of using "SoftMax" as the Activation layer.
- 44
1
vote
Accepted
Using vgg16 or inception with wights equals to None
The advantage of using a pre-trained model without loading the weights (which would mean you are only use the model, not a pre-trained version) is that you can easily use an existing model ...
- 7,507
1
vote
Accepted
Plot a training/validation curve in Pytorch Training
You should use Tensorboard. It has been integrated with PyTorch. See this.
- 682
1
vote
Accepted
What is Deep learning approach to count the number of Diamonds in an image?
I say that go with Object Detection. Because you already have annoted images. And segmentation applied to locate objects and boundaries (lines, curves, etc.) in images. . In Recent days EfficientDet ...
- 273
1
vote
Training Accuracy is getting higher, but Valid Loss and Accuracy is same every epoch
As you can see from the Train and Validation loss (and also accuracy). While your model is able to learn, your validation results do not improve. This means underfitting, or in this specific case, ...
- 1,148
1
vote
Why is VGG16 training accuracy is constant?
some more information is required to gauge why this might be happening.
what is the size of your dataset.
what is your learning rate? From the look at the log it seems like your lr is too low, try ...
- 154
1
vote
Why is VGG16 training accuracy is constant?
You could possibly try varying the learning rate or initialise with different weights. Sometimes the optimiser will get stuck in certain local optimums.
Alternatively try starting with pretrained ...
- 11
1
vote
Why is convnet transfer learning taking so long?
It might have to do with ImageDataGenerator. Data augmentation can be computational expensive. Remove that code and see if the model trains faster.
- 21.2k
1
vote
Accepted
How to fine tuning VGG16 with my own layers
The solution is include Flatten layer to the model:
...
- 241
1
vote
Why are my predictions bad, if my accuracy in train is roughly 100% (Keras CNN)
When getting something like a 100% after 6 epochs, it's almost certain (in my experience at least) that something is wrong at an earlier stage than training...
I would start by debugging and ...
- 131
1
vote
Accepted
Can I save only some VGG19's layers into a .H5 file?
after creating the model you can create another model as below ( I created model till 8 layers)
...
- 556
1
vote
Accepted
1
vote
Neural Network Model using Transfer Learning not learning
The problem with your code is inconsistency of the goal you are willing to operate upon. Softmax calculates the probability of individual neuron and a binary classifier contains single neuron to ...
- 2,415
1
vote
How to save prediction values for the whole data in Keras
First, you could always just wrap you code with a loop.
...
- 366
1
vote
Overfitting in CNN
Based on your accuracies the $12 \%$ difference is introducing high variance problem which means you are overfitting. Due to the fact that the number of parameters is too many for ...
- 14.1k
1
vote
Why is input preprocessing in VGG16 in Keras not 1/255.0
I have a new thinking on this. I think it maybe ok to use a different but reasonable preprocessing (such as 1/255.) in the context of transfer learning (pre-training), than whats originally used to ...
- 221
Only top scored, non community-wiki answers of a minimum length are eligible