Computer Vision Workshop: Using Object Detection for Complex Image Classification Scenarios

Photocredits to the talented Ashley Mcnamara

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.

Getting Started

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

There are six parts to the workshop:

• 1. Introduction
• 2. Policy Classfication With Custom Vision Service
• 3. Policy Recognition with CNNs, MobileNet and Transfer Learning
• 4. Policy Detection with Keras RetinaNet
• 5. Training and Computer Vision Models on the Cloud using Azure ML Service
• 6. Train a Computer Vision Model on a Remote Cluster with Azure Machine Learning

Everything in the workshop is self-contained in docker and will run on a cpu machine.

Setup Instructions

Option 1 Run Locally

Step 1

Download and Install the docker client.

Step 2

Run the following command in the terminal or command prompt

docker run --rm -it -p 8888:8888 abornst/cvworkshop

Step 3

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

Step 4

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

Step 4

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

Option 2 Run On the cloud with the Azure DSVM

The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft’s Azure cloud built specifically for doing data science. It has many popular data science and other tools pre-installed and pre-configured to jump-start building intelligent applications for advanced analytics. While this workshop will run on any cloud it is optimized and tested on the Azure DSVM.

Prerequisite’s

Azure Subscription you can sign up for a trial here

Step 1

Create Linux a DSVM for deployment steps click here

Step 2

Open the Port 8888 on the DSVM

For detailed steps on opening a port click here

Step 3

Connect to the DSVM with the Azure Shell

Step 4

Run Docker Container & link 8888 port to the VM Host using the following command

docker run --rm -it -p 8888:8888 abornst/cvworkshop

Step 5

Now that your Jupyter notebook is running to access it in the browser, copy the link to the local notebook http://cd3cdb8ea05f:8888/?token=66dc6919e8762c8136006cffd90b7b16f3fa7fd1fa591637&token=66dc6919e8762c8136006cffd90b7b16f3fa7fd1fa591637 Replace the http://cd3cdb8ea05f or http://localhost part of the jupyter url with your VM's DNS name

About the Author

Aaron (Ari) Bornstein is an avid AI enthusiast with a passion for history, engaging with new technologies and computational medicine. As an Open Source Engineer at Microsoft’s Cloud Developer Advocacy team, he collaborates with Israeli Hi-Tech Community, to solve real world problems with game changing technologies that are then documented, open sourced, and shared with the rest of the world.