TinyML Starter Kit with ESP32
TinyML Starter Kit with ESP32
The TinyML Starter Kit with ESP32 is a comprehensive development platform designed to facilitate the integration of Artificial Intelligence (AI) and Machine Learning (ML) capabilities into IoT projects. This starter kit combines the robust ESP32 microcontroller with TinyML, a lightweight machine learning framework, to enable the creation of intelligent, connected devices.
| The TinyML Starter Kit with ESP32 allows users to develop and deploy AI-powered IoT projects with ease. The kit enables the following functionalities |
Dual-core 32-bit LX6 microprocessor with 240 MHz clock speed
520 KB SRAM and 4 MB Flash memory
Built-in Wi-Fi and Bluetooth 4.2 capabilities
Supports deployment of TinyML models, enabling real-time inference and machine learning capabilities
Compatible with popular machine learning frameworks, including TensorFlow Lite and TensorFlow Quantization
Supports a wide range of sensors, including temperature, humidity, and gesture recognition sensors
Onboard accelerometer and temperature sensor for convenient data collection
USB interface for programming and debugging
Compatible with popular development platforms, including Arduino, MicroPython, and Lua
Low-power consumption, with a sleep current of less than 5 mA
Supports battery-powered operation for IoT devices
Onboard LED and button for user interface and debugging purposes
Supports SD card storage for data logging and model storage
| The TinyML Starter Kit with ESP32 is suitable for a wide range of IoT applications, including |
To get started with the TinyML Starter Kit with ESP32, users can refer to the comprehensive documentation and tutorials provided. The kit includes everything needed to start developing AI-powered IoT projects, including a detailed user manual, software development tools, and example projects.
TinyML Starter Kit with ESP32 DocumentationOverviewThe TinyML Starter Kit with ESP32 is a powerful and versatile Internet of Things (IoT) component that combines the popular ESP32 microcontroller with the TinyML machine learning framework. This kit enables developers to build intelligent, low-power devices that can run machine learning models at the edge. The ESP32 provides Wi-Fi and Bluetooth connectivity, while TinyML allows for efficient and accurate ML model deployment.Hardware ComponentsESP32 microcontroller board
Breadboard-friendly interface for easy prototyping
Micro-USB connector for programming and power
Onboard Wi-Fi and Bluetooth antennasSoftware ComponentsTinyML framework for machine learning model deployment
ESP32 Arduino core for programming the microcontroller
Example code libraries for various applicationsExample 1: Image Classification with CameraIn this example, we'll use the TinyML Starter Kit with ESP32 to build an image classification system that can recognize objects using a camera module.Hardware RequirementsCamera module (e.g., OV7670)
Breadboard and jumper wires
Power supply (e.g., USB cable)Code Example
```cpp
#include <Arduino.h>
#include <TinyML.h>
#include <WiFi.h>// Initialize camera module
Camera camera(ESP32_CAM_PIN, ESP32_CAM_PIN);// Load TinyML model
TinyMLModel model = TinyMLModel::createFromBuffer(image_classification_model, sizeof(image_classification_model));void setup() {
Serial.begin(115200);
camera.begin();
WiFi.begin("your_wifi_ssid", "your_wifi_password");
while (WiFi.status() != WL_CONNECTED) {
delay(1000);
Serial.println("Connecting to WiFi...");
}
Serial.println("Connected to WiFi");
}void loop() {
// Capture image from camera
camera.capture();
uint8_t imageBuffer = camera.getImageBuffer();
int imageSize = camera.getImageSize();// Preprocess image data
int8_t inputData = new int8_t[imageSize];
for (int i = 0; i < imageSize; i++) {
inputData[i] = imageBuffer[i] / 255.0;
}// Run TinyML inference
int8_t outputData = new int8_t[10]; // assuming 10 output classes
model.run(inputData, imageSize, outputData);// Get classification result
int classIndex = 0;
int maxValue = 0;
for (int i = 0; i < 10; i++) {
if (outputData[i] > maxValue) {
maxValue = outputData[i];
classIndex = i;
}
}Serial.println("Classification result: ");
Serial.println(classIndex);delete[] inputData;
delete[] outputData;
delay(1000);
}
```
Example 2: Anomaly Detection with Sensor DataIn this example, we'll use the TinyML Starter Kit with ESP32 to build an anomaly detection system that can identify unusual patterns in sensor data.Hardware RequirementsSensor module (e.g., DHT11 temperature and humidity sensor)
Breadboard and jumper wires
Power supply (e.g., USB cable)Code Example
```cpp
#include <Arduino.h>
#include <TinyML.h>
#include <WiFi.h>// Initialize sensor module
DHT11 dht(DHT11_PIN);// Load TinyML model
TinyMLModel model = TinyMLModel::createFromBuffer(anomaly_detection_model, sizeof(anomaly_detection_model));void setup() {
Serial.begin(115200);
dht.begin();
WiFi.begin("your_wifi_ssid", "your_wifi_password");
while (WiFi.status() != WL_CONNECTED) {
delay(1000);
Serial.println("Connecting to WiFi...");
}
Serial.println("Connected to WiFi");
}void loop() {
// Read sensor data
float temperature = dht.readTemperature();
float humidity = dht.readHumidity();// Preprocess sensor data
int8_t inputData[2] = {temperature, humidity};// Run TinyML inference
int8_t outputData;
model.run(inputData, 2, &outputData);// Check for anomaly
if (outputData > 0.5) {
Serial.println("Anomaly detected!");
} else {
Serial.println("Normal operation");
}delay(1000);
}
```
These examples demonstrate the capabilities of the TinyML Starter Kit with ESP32 for building intelligent IoT devices that can run machine learning models at the edge. By combining the power of ESP32 with the efficiency of TinyML, developers can create innovative solutions for various applications, including computer vision, natural language processing, and predictive maintenance.