I have a structure for my CNN that will be used in image enhancement. and I want to know how to use Caffe package in Matlab to design and train the network. I don't need to import pretrained network from Caffe as I have a specific structure for the CNN. Does any one have a link or example that guide me on how to do that? Any help will be appreciated.
The Caffe official documentation has a simple example showcasing basic interfaces for Matlab including adding conv layers to the network:
http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab
Once you get the basics for matlab, the interfaces are mostly similar to python.
Hope that helps
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I'm trying to translate a neural network that I wrote in Keras Python, but Matlab says that I can't use the Flatten layer together with the Image Input Layer. How can I solve this problem
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Unfortunately, I could not find an answer to my question on the Internet.
I have a few matlab trained deep learning networks to classify images, and I want to use them in the LabVIEW. And I want to preload the networks at the very beginning, instead of loading the networks between codes. But the MATLAB node in LabView cannot output a network.
I have tried to use the Deep Learning Module in LabView, but the supported tensorflow version is too low for my network to work. I read that DataPlugin for MATLAB could work, but I didn't find any example of how it works.
I am new to matlab and my project requires 1D Convolutional Neural Network. Can anyone tell me how to design 1D CNN in matlab? Does it require any special hardware? Refrence to some blogs will be really helpful.
Thank you.
As I know CNN implementation requires GPU. Matlab 2016 version have some inbuilt function but requires GPU installed to work. Python and tensorflow works great and quick , I would like you suggest to try those than Matlab.
I have some questions about how to actually interact with a pre-trained Caffe model. In my case I'm using a model for scene recognition.
In the caffe git repository, there are some code examples in Python and C++ on the implementations of Image Classifiers. However, those do not apply to my use case (since they only classify the input image as ONE class).
My goal is an application that takes an input image (jpg) and outputs the highest predicted class label for each pixel in the input image (e.i., indices for sky, beach, road, car).
Could anyone give me some pointers on how to proceed?
There already seem to exist implementations for this. This demo (http://places.csail.mit.edu/demo.html) is kind of what I what.
Thank you!
What you are looking for is not image classification, but rather semantic segmentation.
A recent work, by Jonathan Long, Evan Shelhamer and Trevor Darrell is based on Caffe, and can be found here. It uses fully convolutional network, that is, a network with no "InnerProduct" layers only convolutional layers, thus capable of producing outputs with different sizes for different sizes of inputs.
I have a homework to classify multi-class images with Support Vector Machines. I am not allowed to use any toolbox, I have to write SVM code by my self. I have to implement it in MATLAB. Since I am not familiar with MATLAB, I have some troubles about implementing.
Can you suggest me any pseudocode or paper that explains the svm implementation basically? I mean I know the theory of SVM but I am just not good at programming. Or any SVM code might be very helpful!
Thank you for your help in advance.
I like using LibSVM library. On its web pages you can find some useful hints and descriptions of the SVM. There is also beginner's guide to SVM classification. The source code itself should be available as well.
http://www.csie.ntu.edu.tw/~cjlin/libsvm/