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Which GAN model suits best for generating images of beard and no_beard with paired dataset

By : Nicolas Genest
Date : September 14 2020, 06:00 AM
Does that help CycleGAN being a good choice but the reason cycleGAN came out was because paired data is not always possible to collect. If you use that you will unnecessarily train a model which will have to learn A->B translation and also B->A translation when you don't want it to learn B->A translation. Since you have paired data I would suggest you to use pix2pix GAN. You can checkout this github repository.
code :

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WPf which one suits best a dataset or Entity Frame Work?

By : Inmedia Kh
Date : March 29 2020, 07:55 AM
help you fix your problem MVVM is a design pattern that separates the UI from the implementation details.
The Entity Framework is (I believe) an ORM, designed to abstract application specific implementation details from the underlying storage.

R - split and merge paired dataset

By : Vivi An
Date : March 29 2020, 07:55 AM
Hope that helps I would like to split and then merge a paired dataset. , If you create the split list
code :
dtList <- split(dtAct, dtAct$variable)
dtRes <- merge(dtList$tuconjoint, dtList$tutv, by=c('idno','Q2','time'),
           suffixes = c('A','B'))
     idno Q2    time  variableA    valueA    variableB   valueB
  1  561  56    1     tuconjoint   fait      tutv        non fait
  2  561  56    2     tuconjoint   fait      tutv        non fait
  3  562  56    1     tuconjoint   fait      tutv        non fait
  4  562  56    2     tuconjoint   fait      tutv        non fait

Retrain im2txt model with Open Images dataset

By : Ferry Ferry
Date : March 29 2020, 07:55 AM
around this issue While it might be possible to retrain the model, remember that these two data sets come with different labeling semantics and conventions, so understand that the results might not be 100% comparable.
You need to pre-process the data using a script similar to the COCO script.

Which Tensorflow object detection model suits satellite images best?

By : NotSure
Date : March 29 2020, 07:55 AM
Hope that helps The best choice of model depends on your exact requirements and deployment environment. SSD Mobilenet (and other SSD models) perform inference very quickly, but with less accuracy. They are well suited to cases where fast/realtime inference is desirable or situations where computing power is limited (ie. Mobile phones or IoT). In contrast, Faster RCNN or RFCN models will yield more accurate results, but run slower.
Consider trying the Faster RCNN Resnet 101 models. If you want more details check out the model zoo or this paper on the speed accuracy trade-offs of object detection architectures.

TensorFlow dataset from list of images in keras model

By : user2475893
Date : March 29 2020, 07:55 AM
I hope this helps you . Turns out the problem was in using Keras model. The example in TF tutorial relies on Keras model build using tf.keras module (all layers, model, etc. came from tf.keras). While the model I was using (DenseNet) relies on pure keras module, i.e. all layers came from keras module and not from tf.keras. This cause the tf.data.Dataset to be checked for ndim in fit method of keras model. Once I adjusted my DenseNet to use tf.keras layers everything become working again.
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