A tinyML vision model that can classify pictures of people based on whether they are wearing a mask or not.
A smart cat feeder based on esp32s3
Extracting meaningful features from audio signals. How does Edge Impulse MFCC block work?
Use a Nicla Sense ME with an Edge Impulse model attached to the sleeve of a K-way jacket for gesture recognition and bad weather prediction.
Early detection of climate change by monitoring frogs
AI-powered recognition, no cloud, no subscriptions — just smart vision on board
Your license plate is the key. Drive up, gate opens. Remote-key integration guide.
When cat escape from room, send a warning to mobile phone. use SeeedStudio XIAO ESP32S3
Detect impacts in real-time using TinyML with the Arduino Nicla Vision, without modifying a single line of C++ code.
A compact four-legged walking robot powered by Quarky.
This blog contains all the necessary info to optimize your TinyML applications
Revolutionizing Building Maintenance: Introducing GLEWBOT, the Bioinspired Climbing Robot for Flawless Wall Inspection
A watcher device to monitor the room occupation
Most mask detection systems use camera modules but mine works on microphone data.
Automatic FOMO-based label inspection using computer vision and a web app.
This project uses low-sensitivity sensor data to predict temperature and humidity from high-performance sensor using TinyML.
A bird detection system to monitor the activity of birds in a geodesic dome using w5300 ethernet module and stm32F4 Discovery
This TinyML solution recognizes screams and cries of workers who have been caught in an accident
Let's get to know how "Hey Siri" or "Hey Google" work!
Offline at the edge bird classification and reporting over LoRa. Using TinyML for high accuracy many output detection.
WaruGuard: Real-time potato disease detection on Raspberry Pi using a TFLite model. Offline, fast, and farmer-friendly.
Acoustic Guardian uses Edge AI + BLE to detect logging/poaching sounds and alert rangers instantly, protecting forests in real time.
This is a TinyML project based on TensorFlow Lite and Arduino Nano 33 Sense board. Under the instruction of 《TinyML》.
Use TinyML on the M5ATOM to detect motion patterns with the MPU sensor and display numbers on the LED matrix.
