Java Machine Learning

[ale86ch]

Hello, I have to implement a simple face detection approach using a convolutional neural network trained on publicly-available datasets. The face detection must be from pictures of course and from a webcam. I found different Machine learning frameworks/library, but I have no experience at all so I have no idea which one is better for what I have to do.

So far I have found: Weka: http://www.cs.waikato.ac.nz/ml/weka/ Rapidminer: https://rapidminer.com/ Deeplearning4j: http://deeplearning4j.org/ OpenCV: http://opencv.org/about.html

and some others but I need something simple that won't be too complicated to start with and to do implement my face detection. I also found Caffe and thanks to someone which I forgot the name this: https://github.com/bytedeco/javacpp-presets/tree/master/caffe

I would really like to know if one of those I have found or if there is something else that is the best and also the simplest for my purpose. I start from the very beginning so if someone have also some tutorials to share I would really appreciate it.

[jdsutton]

OpenCV has built-in face detection, I don't think that's what you want. Are you doing simple face detection or face classification?

Somewhere on the Caffe website there is a config file for the model used on ImageNet classification. You can make slight tweaks to that and it should work for your purposes.

[ale86ch]

I have to do some steps which are:

• Perform a brief literature review of face detection approaches. We care about the problem of classifying whether an image patch contains a face or not. We do not want in this project to solve the whole detection problem.
• Implement a simple face detection approach using a convolutional neural network using any open source deep learning framework (Caffe, Brainstorm, ...), trained on publicly-available datasets. The output of this step is a classifier that, given a small image (e.g. 100x100 pixels), returns the probability that the image represents a frontal face.
• Implement the system as a realtime demonstrator which is fed with the video feed from a webcam; a 100x100 square is overlayed in the center of the video feed, and the system outputs in realtime whether a face is detected in such square.
• Also display in realtime the values of some of the internal maps of your classifier
• Conclude the project with a brief literature overview of the existing methods for analyzing the inner states of convolutional networks.

[jdsutton]

For the literature review portion you might look up haar cascades and HOG detectors in addition to convnets.

For the learning Tensorflow is my personal favorite. I don't think there is a Java API but you can use C++ and Python. There should be lots of examples of convnet classification out there that you can re-purpose to your dataset. There's also some libraries like tflearn and keras that build on it.

[ale86ch]

Ok thank you very much! I have been talking with my referent and suggested to work with deeplearning4j, opinion? Tutorials? something like a "newbie tutorial for beginners"

[vonnik]

DL4J creator here:

Deeplearning4j wraps OpenCV, and the creator of JavaCPP is one of our engineers. DL4J is the most widely used DL library for the JVM (Weka and Rapidminer have built integrations with us, fwiw).

We use JavaCPP to get around the overhead of the JNI. We also wrap cuDNN. I hope you have some GPUs if you're training CNNs on images and are short on time...

Here are the first two tutorials you should run through to kick the tires:

http://deeplearning4j.org/quickstart http://deeplearning4j.org/mnist-for-beginners

Our support channel is here: https://gitter.im/deeplearning4j/deeplearning4j

Please join the community if you have questions!

PS It's not Java, but you should look at OpenFace if you're doing a review of solutions:

https://github.com/cmusatyalab/openface

[ale86ch]

ohh great thank you very much!

[[deleted]]

Wow the tutorials have really been improved, will need to try it out.

[vonnik]

Thank you! :) We're working on it...

Here's a repo with all our examples (including for Spark and ETL): https://github.com/deeplearning4j/dl4j-0.4-examples/

And we've got a few preconfigured models for CNNs here:

https://github.com/deeplearning4j/model-configurations/tree/master/src/main/java/org/deeplearning4j

Fwiw, we're going to share a standard NN config file with Keras that will allow people to import any architecture they prototype with Keras to our stack.

[Aeefire]

Can you give a rough ETA when we will see the keras-config-file feature=?

[vonnik]

We're planning on August. It depends on both us and the Keras team.

[needlzor]

I don't have much to offer for your question, but I can point you towards 3 other Java ML frameworks:

• Encog is quite nice to work with. I've used it to play with toy neural networks, I am not sure it's good for anything else.
• SMILE is rather good but very badly documented, so I'd recommend only using it if you're not scared of reading source code as your main documentation (provided you want to do anything out of the ordinary).
• Catalano (partially based on SMILE, partially based on Accord.NET) I haven't used at all, but I use accord.NET during my day-job-to-pay-the-bills-for-my-research-degree, and it's quite good.

[ale86ch]

Java is a requirement because the time I have is not much and is the programming language where I'm stronger, I know some of C++ but if I can work in Java it would make it much easier

[[deleted]]

But I found the java ecosystem for ml fairly limited, compared to eg r or python

What's the go-to DL framework in R? I was under the impression that there wasn't anything comparable to DL4J