TensorFlow Chicago Workshop - Building an Object Detection System in Keras

Written By Web Master

The TensorFlow Chicago meetup held a one day workshop to build an object detection system in TensorFlow and Keras.  It was a great opportunity to go beyond the traditional introductory materials in deep learning and learn how a real-life applications are developed in Keras and TensorFlow. 

Approximately 50 participants were made up of Data Scientists and developers from different companies, graduate and PhD students in CS and Predictive Analytics.  Also it was really interesting that the group represented people from all over the world from Russia to Brazil.

Above: Image detection did a great job in detecting many objects in the class !!

 

TensorFlow Workshop 

Github Link HERE

  • Session 1. Introduction to deep learning workflow

    • Objective: Walk students through the process we'll be going through during the workshop

  • Session 2. Basics of Deep Learning

    • Objective: Understanding the main concepts around image classification (convolutional neural networks, transfer learning)

  • Session 3. Object Detection

    • Objective: A quick tour of the main concepts developed in the last few years in object detection, ending with Mask-RCNN in Keras

      Architectures include: R-CNN Fast R-CNN Faster R-CNN Mask R-CNN One shot (YOLO, SSD)

  • Session 4. Model fine-tuning, part 1

    • Objective: Fine-tune a pre-trained model on a narrower set of images

  • Session 5. Dataset creation

    • Objective: Generate a dataset unique to the room and object being detected

  • Session 6. Model fine-tuning, part 2

    • Objective: Fine-tune a pre-trained model on the dataset generated in Session 5

SERVER: AWS EC2 with GPU was provided to the students for a hands-on lab

Instructor Profile

Garrett Smith is founder and lead developer of Guild AI (https://guild.ai), an open source toolkit to streamline TensorFlow and Keras model development. Garrett is veteran of software and systems development, having built tools and managed operations for CloudBees platform-as-a-service. In recent years he has focused on deep learning applications, applying best practices in systems engineering to neural network development and deployment.

Rajiv Shah is a data scientist with DataRobot and an Adjunct Assistant Professor at the University of Illinois at Chicago. He has previously worked as a data scientist for State Farm and Caterpillar. He is an active member of the data science community in Chicago and helps organize the Tensorflow Deep Learning meet up. He has a PhD from the University of Illinois at Urbana Champaign. You can find him on twitter at rajcs4 or his home page http://www.rajivshah.com.

Links to reference material

Web Master
Web Master
https://www.technologyx2.com
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