Videos

Want to turn your NVIDIA Jetson Nano into a web-connected smart camera? This video shows how to set up MaskCam, a mask detection camera implemented with NVIDIA Deepstream, YOLOv4-tiny, and TensorRT, on your own Jetson Nano. It also shows how to set up a local Docker server on your computer that can be used to remotely interact with the Nano and view detection statistics. I worked on this project with BDTI, a technology analysis firm in the Bay Area. The project can serve as a reference design for other connected smart camera applications. It also gives a good example of how to use Docker containers for setting up web databases and using MQTT to communicate detection data. --- Links --- MaskCam Part 1 video, which gives an overview of the hardware and software behind this camera: https://youtu.be/W7SxGhJp8CA GitHub repository: https://github.com/bdtinc/maskcam Link to NVIDIA GTC 2021 conference talk about MaskCam (you need to register to see the talk, but it's free and available on-demand): https://gtc21.event.nvidia.com/media/t/1_etsb5xdn/204677953 MaskCam informational report by BDTI: https://bdti.com/maskcam Link on how to use barrel jack power supply input: https://www.jetsonhacks.com/2019/04/10/jetson-nano-use-more-power/ Amazon Associate link to Jetson Nano: https://amzn.to/3gRTkDc Amazon Associate link to 5V, 4A power supply: https://amzn.to/3t6KP9C --- Music --- Morning Station by Tokyo Music Walker https://soundcloud.com/user-356546060​ Creative Commons — Attribution 3.0 Unported — CC BY 3.0

MaskCam is a really cool project I worked on to evaluate the NVIDIA Jetson Nano's capability for deploying machine learning models on edge devices. It's a connected, machine learning-enabled smart camera that counts the number of masked and unmasked faces in an area and sends the statistics to a remote web server. This video gives a demonstration of MaskCam in action. My colleague and I will be showing this video and talking more about the project at the NVIDIA GTC conference next week. We'll also be presenting it at the Embedded Vision Summit in May. To learn more about MaskCam, please see the links below! How to set up MaskCam on your own Jetson Nano: https://youtu.be/Ow4wiGXFJi8 Link to detailed report on MaskCam: https://www.bdti.com/maskcam Link to GitHub repo: https://github.com/bdtinc/maskcam Link to NVIDIA GTC 2021 conference talk (you need to register to see the talk, but it's free): https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s32588/ Link to Embedded Vision Summit presentation: will be added when it's ready! I worked on this project with BDTI and Tryolabs, which are both excellent consulting companies in the machine learning and computer vision space. Check out their sites if you'd like to learn more about them! BDTI: https://www.bdti.com Tryolabs: https://www.tryolabs.com

Want to achieve blazing fast detection speeds (30+ FPS) with your TensorFlow Lite models on the Raspberry Pi? This video shows how to set up Google's Coral USB Accelerator with the Raspberry Pi to run TFLite object detection models on videos or real-time webcam feeds. It works for the Pi 3B+ and the Pi 4. Have questions? Ask me on Twitter @EdjeElectronics ! I usually respond faster there: https://twitter.com/EdjeElectronics -- Affiliate Links -- Get a Rasbperry Pi 4: https://amzn.to/2Kf0el8 Coral USB Accelerator from Google: https://amzn.to/2wxTZ8d Coral USB Accelerator from seeed studio (sometimes less expensive): https://amzn.to/3bBVY9Z Webcam used in this video (works better than the Picamera!): https://amzn.to/2MMBTU3 ---- Commands used in video ---- echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list curl packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add - wget dl.google.com/coral/canned_models/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite -- Tutorial Links -- Previous TensorFlow Lite video: https://youtu.be/aimSGOAUI8Y Written version of this guide: https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/blob/master/Raspberry_Pi_Guide.md Guide for training your own TFLite model: https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi Performance comparison between Pi 3B+ and Pi 4 4GB: https://youtu.be/TiOKvOrYNII -- Music credit -- chill. by sakura Hz https://sakurahertz.bandcamp.com/track/chill Creative Commons — Attribution 3.0 Unported — CC BY 3.0

TensorFlow Lite is a framework for running lightweight machine learning models, and it's perfect for low-power devices like the Raspberry Pi! This video shows how to set up TensorFlow Lite on the Raspberry Pi for running object detection models to locate and identify objects in real-time webcam feeds, videos, or images. I used a Raspberry Pi 4 4GB for this video, but it also works with the Raspberry Pi 3. If you want to see how much faster the Pi 4 is than the Pi 3, check out my performance comparison video: https://youtu.be/TiOKvOrYNII Have questions? Ask me on Twitter @EdjeElectronics ! I usually respond faster there: https://twitter.com/EdjeElectronics -- Affiliate Links -- Get a Rasbperry Pi 4: https://amzn.to/2Kf0el8 Coral USB Accelerator: https://amzn.to/2wxTZ8d Webcam used in this video (works better than the Picamera!): https://amzn.to/2MMBTU3 -- Tutorial Links -- UPDATE (10/21/20): At 6:09 in the video, I instruct you to go to the TensorFlow Lite Object Detection Overview page and right click the "Download starter model" link to copy the link address. The page has changed since I made this video, and that link is no longer correct. Copy this link for downloading the starter model: storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip Written version of this guide: https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/blob/master/deploy_guides/Raspberry_Pi_Guide.md Guide for training your own TFLite model: https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi -- Music credit -- The Process by LAKEY INSPIRED: https://soundcloud.com/lakeyinspired/the-process Creative Commons — Attribution-ShareAlike 3.0 Unported — CC BY-SA 3.0

Have you wondered how much faster the Raspberry Pi 4 performs than the Raspberry Pi 3 at running computationally intensive TesnorFlow object detection models? This video gives a performance comparison between the Pi 3B+ and the Pi 4 4GB, showing what framerate is achieved when running TensorFlow and TensorFlow Lite SSD-MobileNet detection models. It also shows how much faster the models run when using Google's Coral USB Accelerator. This is the first video in a larger series of TensorFlow Lite videos I'm working on. The series will show how to train your own TensorFlow Lite models and run them on the Raspberry Pi, Android devices, and more. Stay tuned! Raspberry Pi 4 4GB starter kit: https://amzn.to/2Kf0el8 Coral USB Accelerator: https://amzn.to/2BuG1Tv Webcam used in this video (works better than the Picamera!): https://amzn.to/2MMBTU3 Have questions? Ask me on Twitter @EdjeElectronics ! I usually respond faster there: https://twitter.com/EdjeElectronics GitHub guide showing how to set up TensorFlow Lite and Coral USB Accelerator on the Raspberry Pi: https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/blob/master/deploy_guides/Raspberry_Pi_Guide.md GitHub guide showing how to train and convert your own TensorFlow Lite model: https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi ---- Music credit ---- Flamingo by jlsmrl: https://soundcloud.com/jlsmrl/free-guitar-type-beat-flamingo Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Merry Bay by Ghostrifter Official: https://soundcloud.com/ghostrifter-official/merry-bay Creative Commons — Attribution-ShareAlike 3.0 Unported — CC BY-SA 3.0 Coffee Dreams by Le Gang [Audio Library Release]: https://www.youtube.com/watch?v=JAL3i9ZIwQo Music provided by Audio Library Plus

This quick video shows how to install the CanaKit heatsinks on the Raspberry Pi 4. It also gives a brief comparison of thermal performance with and without the heatsinks installed. The CanaKit Raspberry Pi 4 kits don't provide information on where to put the heatsinks. I couldn't easily find the info elsewhere online, so I made this video to give clear instructions on where the heatsinks should be placed. If you'd like to set up TensorFlow object detection on your Pi, you can follow the tutorial here: https://www.youtube.com/watch?v=npZ-8Nj1YwY -- Amazon Affiliate Links -- Get the CanaKit Raspberry Pi 4 4GB Starter Kit here: https://amzn.to/2Kf0el8 Pimoroni Fan Shim for Raspberry Pi: https://amzn.to/2Kya94d Link to blog post on Tom's Hardware: https://www.tomshardware.com/reviews/pimoroni-fan-shim-heatsink-raspberry-pi-4,6219.html Have questions? Ask me on Twitter @EdjeElectronics ! I usually respond faster there: https://twitter.com/EdjeElectronics

RAIN MAN 2.0 is a card counting AI that's destined to be the ultimate blackjack player! Created using machine learning and Python, RAIN MAN 2.0 can easily count his way through a deck of playing cards. To identify cards, he uses a YOLO v3 detection model trained on 50,000 synthetically generated images. This video explains how he works and what I plan to do with him. It's the first video in a series of videos that I will be posting as I build out his functionality. --- Affiliate Links --- If you haven't seen the movie Rain Man, you need to watch it!! https://amzn.to/2XL7bit Logitech C920 1080p webcam used by RAIN MAN 2.0: https://amzn.to/2WULb7B Twitter: https://twitter.com/EdjeElectronics RAIN MAN 2.0 project page on Hackaday: https://hackaday.io/project/27639-rain-man-20-blackjack-robot --- Links mentioned in video --- geaxgx1's playing card detection video: https://youtu.be/pnntrewH0xg Wizard of Odds website, great source of blackjack information and strategy: https://wizardofodds.com/games/blackjack/card-counting/high-low/ --- Music --- Summer Coffee by Barradeen: https://soundcloud.com/barradeen/summer-coffee Hidden camera blackjack footage taken from Blackjack Army YouTube channel: https://youtu.be/Ri2dS3GkzH0

I used TensorFlow and a Raspberry Pi to create a pet detector camera that watches the door and texts me when my cat wants to be let inside! This video explains how the code works, so you can use it as an example for your own object detection application. Get a Raspberry Pi: https://amzn.to/2Iki3fb Get a Picamera: https://amzn.to/2rKxarh Handy Picamera + Pi case: https://amzn.to/2LxaUed If you have questions, I usually respond more quickly on Twitter, so send me a tweet @EdjeElectronics ! https://twitter.com/EdjeElectronics ---- Links mentioned in video ---- Pet Detector Python code: https://github.com/EdjeElectronics/TensorFlow-Object-Detection-on-the-Raspberry-Pi/blob/master/Pet_detector.py Video tutorial on how to set up TensorFlow on the Raspberry Pi: https://youtu.be/npZ-8Nj1YwY Twilio guide: https://www.twilio.com/docs/sms/quickstart/python Music: Broke For Free - Something Elated http://brokeforfree.com/

Learn how to install TensorFlow and set up the TensorFlow Object Detection API on your Raspberry Pi! These instructions will allow you to detect objects in live video streams from your Picamera or USB webcam. Get a Raspberry Pi: https://amzn.to/2Iki3fb Get a Picamera: https://amzn.to/2rKxarh Handy Picamera + Pi case: https://amzn.to/2LxaUed If you have questions, I usually respond more quickly if you send me a tweet on Twitter: @EdjeElectronics https://twitter.com/EdjeElectronics I created this video using a Raspberry Pi 3 Model B running Raspbian Stretch. It should also work for the Raspberry Pi 2. ---- Link to steps in video ---- 1:00 Step 1. Update the Raspberry Pi 1:38 Step 2. Install TensorFlow 4:14 Step 3. Install OpenCV 6:03 Step 4. Compile and Install Protobuf 10:29 Step 5. Set up TensorFlow directory structure 14:39 Step 6. Test out object detector! ---- Links mentioned in video ---- Written version of this tutorial on GitHub: https://github.com/EdjeElectronics/TensorFlow-Object-Detection-on-the-Raspberry-Pi lhelontra's "TensorFlow on ARM" repository: https://github.com/lhelontra/tensorflow-on-arm/releases OSDevLab's guide to building Protobuf on the Pi: http://osdevlab.blogspot.com/2016/03/how-to-install-google-protocol-buffers.html GitHub Protobuf releases page: https://github.com/google/protobuf/releases TensorFlow Model Zoo: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md Card detector model on DropBox: https://www.dropbox.com/s/27avwicywbq68tx/card_model.zip?dl=0 Music: Broke For Free - My Always Mood http://brokeforfree.com/