Today I want to show you how Joker can be used for neural network image classification with Caffe project. Caffe is a deep learning framework. This tutorial should work on any Linux distribution (CoreOS, Debian, RedHat, etc).
For simplify overall process I have created Docker container with Caffe (built from sources) and already trained “BVLC CaffeNet Model” (based on ImageNet). Who wants to dive deeper can check training instructions here.
Let’s do some console work. Issue following command to pull and run docker container with required software:
docker run --name ai -p 5000:5000 aospan/caffe-cpu python /opt/caffe/examples/web_demo/app.py
After container startup you can go to web-interface:
and check how neural network classify your images. Let’s try to check how our neural network can classify images. I will make some photos from my smartphone and will check:
hmm, looks good ! Our neural network found tennis ball on the image (i have marked results by red arrow). Let’s try one more:
Bicycle was found ! Try to make photos by yourself and play with AI (neural network).
Neural network image classification performance
Using ‘examples/cpp_classification/’ from Caffe package I have achieved 2 images per second for classification task. This basically enough for practical use, for example, for home automation or IoT tasks.