#raspberrypi #wildlifecamera #tensorflow #tensorflowlite

In this tutorial I will show you how you can set up your own wildlife camera using a Raspberry PI 4 and Tensorflow Lite.
All the source code and written steps for this tutorial can be found in this blog post:

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Creating a Wildlife Camera with A Raspberry PI, Python, Tensorflow and OpenCV
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Sony Vlog Camera ZV1
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Rode Wireless GO
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LIGHTING - Neewer 3 Packs 530 RGB Led Light with APP Control
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Manfrotto Variable Friction Arm with Bracket, Super Clamp
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Raspberry Pi HQ Camera
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Deep Learning and Gaming PC COMPONENTS:

AMD Ryzen 5900X CPU
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Gigabyte GeForce RTX 3070 EAGLE OC 8GB Graphics Card
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ASUS ROG Strix X570-E Gaming ATX Motherboard, AMD Socket AM4
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Corsair iCUE H150i PRO XT RGB Liquid CPU Cooler (360mm)
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Crucial Solid State Drive 1TB
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PC Case - Phanteks Eclipse P600S
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Memory - Corsair Vengeance RGB Pro 32GB (2x16GB) DDR4 3600- Black
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Power Supply - CORSAIR CX750F RGB - 750 Watt, Fully Modular
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Timestamps:

0:00 Introduction
0:55 What is Tensorflow Lite?
3:07 How to run Tensorflow Lite on the Raspberry PI for Object Detection
3:29 Updating the Raspberry PI packages
3:58 Rebooting the Raspberry PI
4:03 Installing Docker
4:19 Installing Docker Compose
4:38 Downloading Source Code with git clone
5:13 Explaining important parameters in docker-compose.yaml
6:00 Specifying objects to detect and detection thresholds
7:14 Enabling or disabling Google Edge TPU
8:20 Configuration for notifications via Slack
9:03 The code that downloads the models.
9:32 Running the wildlife camera for the first time
10:26 Starting the camera in debug mode

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