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

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

Clone the repo

git clone https://github.com/SherifElMahdi/CVWorkshop.git

Ask your proctor for the TAR file then load it using the following command:

docker load < cv_workshop.tar

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

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

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

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