Note this workshop has been deprecated

Check out the new workshop with full CPU support, Keras examples, RetinaNet support explinations of the latest Azure Machine Learning Service here

Computer Vision Workshop

In this workshop we'll be exploring the topic of Computer Vision, through deep diving into a recent real world customer scenario. We’ll compare different approaches and demonstrate how the open source VoTT (Visual Object Tagging Tool) can be used to easily annotate image and quickly iterate object detection models for complex image classification scenarios.

This computer vision workshop is based on the work detecting complex policies in the following real life code story

Setup Instructions

Step 1

Download and Install the docker or if you have a gpu and unix based os the nvidia-docker client.

Step 2

Clone or download the Computer Vision Workshop repo

Step 3

OPTION A From TAR File Load from the tar file with the following command

docker load < cv_workshop.tar

OPTION B NO TAR File If you don't have tar file. Build the workshop docker image using the following command for either cpu or gpu.

CPU

docker build -f Dockerfile-py3-cpu . -t cv

GPU

nvidia-docker build -f Dockerfile-py3-gpu . -t cv

Step 4

Run the image you built using the following command for either cpu or gpu to start the notebook server. If you are on windows make sure you are running linux containers.

CPU

sudo docker run -it -v /var/run/docker.sock:/var/run/docker.sock -p 8888:8888 --expose=8888 cv

GPU

sudo nvidia-docker run -it  -v /var/run/docker.sock:/var/run/docker.sock -p 8888:8888 --expose=8888 cv

Step 5

Copy and store the notebook token key that is displayed after the notebook server is running

Step 6

Navigate to http://localhost:8888/tree and enter the token you copied.

Step 7

Click on the "Computer Vision Workshop Intro" notebook and confirm that everthing loads as expected