Now, I am trying to install caffe with matlab interface. I am able to build it with a gcc-5 warning but I am unalbe to make test it. I am running into errors given below: My system config: Ubuntu -16.04, opencv 2.4.9, gcc-5, g-5, Matlab2017b. Here's a simple script that loads up some default images and runs them through the imagenet classifier (matcaffedemo). However, I should warn you that Caffe's Matlab interface doesn't support as rich a feature set as the Python/C interfa.
This article was originally posted here: Deep-Learning (CNN) with Scilab – Using Caffe Model by our partner Tan Chin Luh.
You can download the Image Processing & Computer Vision toolbox IPCV here: https://atoms.scilab.org/toolboxes/IPCV
In the previous post on Convolutional Neural Network (CNN), I have been using only Scilab code to build a simple CNN for MNIST data set for handwriting recognition. In this post, I am going to share how to load a Caffe model into Scilab and use it for objects recognition.
This example is going to use the Scilab Python Toolbox together with IPCV module to load the image, pre-process, and feed it into Caffe model to recognition. I will start from the point with the assumption that you already have the Python setup with caffe module working, and Scilab will call the caffe model from its’ environment. On top of that, I will just use the CPU only option for this demo.
Let’s see how it works in video first if you wanted to:
Let’s start to look into the codes. S. carey hoyas ep rarely.
The codes above will import the python libraries and set the caffe to CPU mode.
This will load the caffe model, the labels, and also the means values for the training dataset which will be subtracted from each layers later on.
Initially the data would be reshape to 3*227*227 for the convenient to assign data from the new image. (This likely is the limitation of Scipython module in copying the data for numpy ndarray, or I’ve find out the proper way yet)
This part is doing the “transformer” job in Python. I personally feel that this part is easier to be understand by using Scilab. First, we read in the image and convert it to 227 by 227 RGB image. This is followed by subtracting means RGB value from the training set from the image RGB value resulting the data from -128 to 127. (A lot of sites mentioned that the range is 0-255, which I disagreed).
This is followed by transposing the image using permute command, and convert from RGB to BGR. (this is how the network sees the image).
In this 3 lines, we will reshape the input blob to 1 x 154587, assign input to it, and then reshape it to 1 x 3 x 227 x 227 so that we could run the network.
Finally, we compute the forward propagation and get the result and show it on the image with detected answer.
A few results shown as below:
protofile
— File name
character vector | string scalar
File name of the
.prototxt
file containing the network architecture, specified as a character vector or a string scalar. protofile
must be in the current folder, in a folder on the MATLAB® path, or you must include a full or relative path to the file. If the .prototxt
file does not specify the size of the input data, you must specify the size using the 'InputSize'
name-value pair argument.Example:
'digitsnet.prototxt'
datafile
— File name
character vector | string scalar
File name of the
.caffemodel
file containing the network weights, specified as a character vector or a string scalar. datafile
must be in the current folder, in a folder on the MATLAB path, or you must include a full or relative path to the file. To import network layers without weights, use importCaffeLayers
.Example:
'digits_iter_10000.caffemodel'
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Name-Value Pair Arguments
Specify optional comma-separated pairs of
Example: Name,Value
arguments. Name
is the argument name and Value
is the corresponding value. Name
must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,..,NameN,ValueN
.importCaffeNetwork(protofile,datafile,'AverageImage',I)
imports a pretrained network using the average image I
for zero-center normalization.Caffe Matlab Free
Size of input data, specified as a row vector. Specify a vector of two or three integer values
[h,w]
, or [h,w,c]
corresponding to the height, width, and the number of channels of the input data. If the .prototxt
file does not specify the size of the input data, then you must specify the input size.Example:
[28 28 1]
Caffe Matlab Code
Average image for zero-center normalization, specified as amatrix. If you specify an image, then you must specify an image ofthe same size as the input data. If you do not specify an image, thesoftware uses the data specified in the
.prototxt
file,if present. Otherwise, the function sets the Normalization
propertyof the image input layer of the network to 'none'
.Caffe Matlab Install
'Classes'
— Classes of the output layer
'auto'
(default) | categorical vector | string array | cell array of character vectors
Caffe Python Interface
Classes of the output layer, specified as a categorical vector, string array, cell array of character vectors, or
'auto'
. If you specify a string array or cell array of character vectors str
, then the software sets the classes of the output layer to categorical(str,str)
. If Classes
is 'auto'
, then the function sets the classes to categorical(1:N)
, where N
is the number of classes.Caffe Matlab File
Data Types:
char
| categorical
| string
| cell