本文介绍了弱监督目标定位的CAM算法,并基于tflite-micro示例项目"person_detection"训练和部署到ESP32模组上,实现了基于CAM的人体检测与定位功能。
This seemingly simple paradigm has led to groundbreaking advancements in complex tasks like forecasting, anomaly detection, and computer vision! 📚 Learn More Recommended Boards Supporting TinyML Seeed Studio XIAO ESP32S3 Sense Ultra-small ESP32-S3 development board with OV2640 camera, a ris...
UNIHIKER K10 is a low-cost STEM education platform for TinyML applications that leverages the ESP32-S3 wireless microcontroller with vector extensions for workloads such as image detection or voice recognition. The UNIHIKER K10 also features a built-in 2.8-inch color display, a camera, a speaker...
raspberry-piarduinodeep-learningesp32pytorchimage-classificationobject-detectionjetsonncnnonnxtflitetinymlyolov5openmmlab UpdatedMar 6, 2025 Python kartben/artificial-nose Star358 Code Issues Pull requests Instructions, source code, and misc. resources needed for building a Tiny ML-powered artificial nose....
TinyML: Analysis of Xtensa LX6 microprocessor for Neural Network Applications by ESP32 SoC |[pdf] [Keyword Transformer]: A Self-Attention Model for Keyword Spotting |[pdf] LB-CNN: An Open Source Framework for Fast Training of Light Binary Convolutional Neural Networks using Chainer and Cupy |[...
Like the original classification model, just now with bounding boxes and you label each object to detect In-Person Course on Actuators using the XiaoEsp32s3-sense Note: soldered headers are now needed on the XiaoEsp32s3-sense Students are encouraged to work ahead of the class. TopicExample ...