How to Deploy Computer Vision Models to Raspberry Pi with Docker
Step-by-step computer vision model deployment tutorial.
0:00 - 2:35 - Server Setup and Model Selection
2:35 - 4:28 - Running Inference on a Single Image
4:28 - 6:30 - Formatting, Interpreting and Improving Inference Results
6:30 - 9:50 - Customization Walkthrough + Reading Predictions on Multiple Images or a Folder of Images
9:50 - 11:10 - Compatible Models, Performance Expectations and Improvements
11:10 - 12:40 - Next Steps + More Example Code and Projects
Roboflow supports deploying custom computer vision models to Raspberry Pi devices with a performance optimized Docker container. A Raspberry Pi is often used as an edge device because of their price point, size, power consumption, offline capability, and more.
Documentation: https://docs.roboflow.com/inference/r...
Blog post: https://blog.roboflow.com/deploy-comp...
Deploying YOLOv8 models to Raspberry Pi: https://blog.roboflow.com/how-to-depl...
More project examples: https://blog.roboflow.com/raspberry-p...
Hosted API: https://docs.roboflow.com/inference/h...
Python package: https://docs.roboflow.com/python-package