jiteshsaini/model_garden
Performance testing of 24 Machine Learning models on Raspberry Pi using TensorFlow Lite and Google Coral USB Accelerator
This project helps developers and hobbyists evaluate the real-world performance of various pre-trained machine learning models on low-power devices. It takes camera input on a Raspberry Pi and outputs live video with overlays, showing the classification or object detection results from different models. This is ideal for embedded systems developers or robotics enthusiasts building edge AI applications.
No commits in the last 6 months.
Use this if you need to quickly test and compare the inference speed and accuracy of different TensorFlow Lite computer vision models on Raspberry Pi hardware, optionally leveraging a Google Coral USB Accelerator.
Not ideal if you are looking to train custom models or deploy these models on cloud infrastructure or high-performance computing setups.
Stars
22
Forks
7
Language
Python
License
—
Category
Last pushed
Sep 07, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/jiteshsaini/model_garden"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
huggingface/knockknock
🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code
collabnix/pico
Object Detection and Analysis Made easy using Raspberry Pi, Apache Kafka, AWS Rekognition & Docker
bensonruan/Color-Tracking
Color Tracking with tracking.js
HichTala/draw2-plugin
DRAW 2 Plugin for OBS, to integrate Yu-Gi-Oh! cards detector into your live streams and videos
YaelBenShalom/Objects-Recognition-and-Classification
Objects recognition and classification using machine learning, computer vision and real-time...