I am implementing a facial expression recognition and am using SVM to classify given expression.
When I train, I use this command line
svm.train(myFeatureVector,myLabels,Mat(),Mat(), myParameters);
svm.save("myClassifier.yml");
which will later when I will predict using
response = svm.predict(incomingFeatureVector);
But then when I want to train more than once (exited the program and start again), it seems to have overwritten my previous svm file. Is there any way I could do read previous svm file and add more data into it (and then resave it ,etc) ? I looked up on this openCV documentation and found nothing. However, when I read on this page; there is a method called CvSVM::read. I don't know what that does/how to implement it.
Hope anyone can help me :(
What you are trying to do is incremental learning but unfortunately Support Vector Machines is a batch algorithm, hence if you want to add more data you have to retrain with the whole set again.
There are online learning alternatives, like Pegasos SVM but I am not aware of any that is implemented on OpenCV
Related
C++ examples of MXNet contain model training examples for MNISTIter, MNIST data set (.idx3-ubyte or .idx1-ubyte). However the same code actually recommends to use im2rec tool to produce the data, and it produces the different .rec format. Looks like the .rec format contains images and labels in the same file, because im2rec takes a prepared .lst file with both (number, label and image file name per each line).
I have produced the code like
auto val_iter = MXDataIter("ImageRecordIter");
setDataIter(&val_iter, "Train", vector < string >
{"output_train.rec", "output_validate.rec"}, batch_size));
with all files present but it fails because four files are still required in the vector (segmentation fault). But why, should not labels be inside the file now?
Digging more into the code, I found that setDataIter actually sets the parameters. Parameters for ImageRecordIter can be found here. I tried to set parameters like path_imgrec, path.imgrec, then call .CreateDataIter() but all this was not helpful - segmentation fault on the first attempt to use the iterator.
I was not able to find a single example in the whole Internet about how to train any MxNet neural network in C++ using .rec file format for training and validation sets. Is it possible? The only work around I found is to try original MNIST tools that produce files covered by MNIST output examples.
Eventually I have used Mnisten to produce the matching data set so that may input format is now the same as MxNet examples use. Mnisten is a good tool to work, just it is important not to forget that it normalizes grayscale pixels into 0..1 range (no more 0..255).
It is a command line tool but with all C++ code available (and there is not really a lot if it), the converter can also be integrated with existing code of the project to handle various specifics. I have never been affiliated with this project before.
I'm still very new to the world of machine learning and am looking for some guidance for how to continue a project that I've been working on. Right now I'm trying to feed in the Food-101 dataset into the Image Classification algorithm in SageMaker and later deploy this trained model onto an AWS deeplens to have food detection capabilities. Unfortunately the dataset comes with only the raw image files organized in sub folders as well as a .h5 file (not sure if I can just directly feed this file type into sageMaker?). From what I've gathered neither of these are suitable ways to feed in this dataset into SageMaker and I was wondering if anyone could help point me in the right direction of how I might be able to prepare the dataset properly for SageMaker i.e convert to a .rec or something else. Apologies if the scope of this question is very broad I am still a beginner to all of this and I'm simply stuck and do not know how to proceed so any help you guys might be able to provide would be fantastic. Thanks!
if you want to use the built-in algo for image classification, you can either use Image format or RecordIO format, re: https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html#IC-inputoutput
Image format is straightforward: just build a manifest file with the list of images. This could be an easy solution for you, since you already have images organized in folders.
RecordIO requires that you build files with the 'im2rec' tool, re: https://mxnet.incubator.apache.org/versions/master/faq/recordio.html.
Once your data set is ready, you should be able to adapt the sample notebooks available at https://github.com/awslabs/amazon-sagemaker-examples/tree/master/introduction_to_amazon_algorithms
So...
Where can I find completed img's pack for training opencv face recognizing system?
Can anybody help?
have a look here
the att faces db was probably used a lot ( if you look at the docs. )
once you downloaded a set of images, you'll want to run the little python script to generate the needed csv file for training
if you opt for the yale db, you'll have to convert the images to png or pgm first ( opencv can't handle gif's)
but honestly, in the end you want to use a db, that consists entirely of faces you want to recognize [that is, your own db].
unlike most ml algo's it does not need explicit 'negative' images[people other than you want to recognize] here. thoose only add noise and degrade the actual recognition.
the only situation, where you'd want that is when there's only 1 person to recognize. you#d need some others there to increase 'contrast'
I'm looking for advices, for a personal project.
I'm attempting to create a software for creating customized voice commands. The goal is to allow user/me to record some audio data (2/3 secs) for defining commands/macros. Then, when the user will speak (record the same audio data), the command/macro will be executed.
The software must be able to detect a command in less than 1 second of processing time in a low-cost computer (RaspberryPi, for example).
I already searched in two ways :
- Speech Recognition (CMU-Sphinx, Julius, simon) : There is good open-source solutions, but they often need large database files, and speech recognition is not really what I'm attempting to do. Speech Recognition could consume too much power for a small feature.
- Audio Fingerprinting (Chromaprint -> http://acoustid.org/chromaprint) : It seems to be almost what I'm looking for. The principle is to create fingerprint from raw audio data, then compare fingerprints to determine if they can be identical. However, this kind of software/library seems to be designed for song identification (like famous softwares on smartphones) : I'm trying to configure a good "comparator", but I think I'm going in a bad way.
Do you know some dedicated software or parcel of code doing something similar ?
Any suggestion would be appreciated.
I had a more or less similar project in which I intended to send voice commands to a robot. A speech recognition software is too complicated for such a task. I used FFT implementation in C++ to extract Fourier components of the sampled voice, and then I created a histogram of major frequencies (frequencies at which the target voice command has the highest amplitudes). I tried two approaches:
Comparing the similarities between histogram of the given voice command with those saved in the memory to identify the most probable command.
Using Support Vector Machine (SVM) to train a classifier to distinguish voice commands. I used LibSVM and the results are considerably better than the first approach. However, one problem with SVM method is that you need a rather large data set for training. Another problem is that, when an unknown voice is given, the classifier will output a command anyway (which is obviously a wrong command detection). This can be avoided by the first approach where I had a threshold for similarity measure.
I hope this helps you to implement your own voice activated software.
Song fingerprint is not a good idea for that task because command timings can vary and fingerprint expects exact time match. However its very easy to implement matching with DTW algorithm for time series and features extracted with CMUSphinx library Sphinxbase. See Wikipedia entry about DTW for details.
http://en.wikipedia.org/wiki/Dynamic_time_warping
http://cmusphinx.sourceforge.net/wiki/download
I need to do a 3-fold cross validation using Joaquim's SVM light. Cross Validation and SVM are new things to me and I don't know if I'm doing it right. What have I done so far? I converted my data in 3 files that I called fold1.txt fold2.txt fold3.txt with my features in this following model:
1 numberofthefeature:1 numberofthefeature:1 ...
And I also did a file called words.txt with my tokens where the number of the lines are my numberofthefeature. Did I do everything right?
So, now I have to do the 3-fold cross-validation, but I don't know how to do it with Joaquim's SVM light. I don't know to make the svm light learn and classify using the three files and choose which ones I'm going to use as a test and a train. Do I have to do a script or a program to do it?
Thanks to everybody
Thiago
I am gonna assume that you are doing text-mining as you are referring to Thorsten Joachims. Anyways, here is a set of tutorial videos on text classification, with x-validation:
http://vancouverdata.blogspot.ca/2010/11/text-analytics-with-rapidminer-part-5.html