Data Con LA 2019 - Real Time Face Detection through a Webcam Using AI by Obaid Sarvana
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In this session we will provide an introduction to TensorFlow.js. We will then use a step by step walkthrough for building a fully functional real time face detection using nothing but a webcam, a browser, Tensorflow.js and face-api.js.
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Data Con LA 2019 - Real Time Face Detection through a Webcam Using AI by Obaid Sarvana
- 1. ACCENTURE APPLIED INTELLIGENCE Accenture’s unique approach to combiningAI with data, analytics and automation under a bold strategic vision to transform your business—not in silos, but across every function and every process, at scale.
- 2. AGENDA • Introductions • Brief background on Machine Learning &Tensorflow • Edge Learning • Tensorflow & FaceAPI • Code Demo • Come connect with us
- 3. WHAT AREWETALKINGABOUT? In this session we will provide an introduction toTensorFlow.js.We will then use a step by step walkthrough for building a fully functional real time face detection using nothing but a webcam, a browser,Tensorflow.js and face-api.js. Want to learn more aboutTensorflow?Check out Google’sCrash course! https://developers.google.com/machine-learning/crash-course/
- 4. REACH OUT TO CONNECT! WHO ARE WE? OBAID SARVANA obaid.sarvana@accenture.com JACOB REDDING Jacob.redding@accenture.com Data Architects, Software developers Latest Projects • J: Open Source strategies/Governance • O: Software Innovation, Connected Health, Innovation Strategy
- 5. HOW DIDWE GET HERE? Jungle > Candy Store AI Packaged goods Learning on the edge!
- 6. Compute locally! • https://searchenterpriseai.techtarget.com/news/252461988/Machine-learning-on- edge-devices-solves-lack-of-data-scientists • "Things are going to get smart because they process their own data and formulate theories about the world," Crosby said. LEARNING ONTHE EDGE
- 8. FACEAPI
- 9. MAKES IT EASY…EASIER….ISH The packages make it “Easier”, but, of course, that is relative. Let’s dig into the code and we can discuss the current state of software and where we are and where this is going.
- 10. CODE DEMO LET’S DIG IN
- 11. CODE DEMO FLOW • Browser • enable webcam • enable tensorflow • enable facial recognition (faceapi) • enable face marker (Faceapi) • enable emotion detection (faceapi) • End result - website with facial recognition + markers + detection
- 12. THANKYOU
- 14. COME HANG OUT WITH US (WE’LL BE AT THE BOOTH)
Notes de l'éditeur
- Machine Learning – not a Jungle, a candy shop Feels dangerous, scary Still have math PhDs and superexperts, but their findings and insights are now getting translated to more accessible tools Actually – getting easier and easier to access This shift is driven by 2 phenomena: (funded) AI summer – Google, Facebook, etc. whose bottom lines are helped by AI, are funding investments into AI (pyTorch, Tensorflow, etc) API revolution – developers, and technologists exposing an abstraction of their knowledge/tools to 1) protect their IP and 2) Allow for easy use/access their tool in a way that lets’ them hit the groud running Leading to high quality AI tools, which can be accessed through APIs requiring minimal knowledge of AI Math etc Democratization of AI As these packages mature, they are also getting ported to other languages, and in the process becoming more and more lightweight. Allowing for the reverse of the shift to cloud. We’ve heard about the rise of edge computing, but now we’re seeing the enablement of edge learning. Smart things that don’t need to rely on master control - browsers, phones, raspberry Pis that can learn and formulate their own theories of the world What was the paradigm shift that led to the current explosion Brief historical reference (pre-tensor learning and post-tensor) 2 phenoms – Tensorflow (Keras, etc.) & Edge Learning Software packages do the heavy lifting so you can focus on the experimentation Today – Edge learning (edge compute) Code Demo