Student Robotics, Systems and Control at ETH Zürich
With the recently released official Tensorflow 2 support for the Tensorflow Object Detection API, it's now possible to train your own custom object detection models with Tensorflow 2.
Learn how to use the Tensorflow Object Detection API with Tensorflow 2
The Arduino Nano 33 BLE Sense is an evolution of the traditional Arduino Nano, but featuring a lot more powerful processor, the nRF52840 from Nordic Semiconductors, a 32-bit ARM® Cortex™-M4 CPU running at 64 MHz.
Use TensorRT to run PyTorch models on the Jetson Nano.
Run Tensorflow model on the Jetson Nano by converting them into TensorRT format.
YOLO Object Detection on the Jetson Nano using TensorRT
The NVIDIA Jetson Nano Developer Kit is a small edge computer for AI development. The Jetson Nano Developer Kit packs a Quad-core ARM A57 CPU with a clock-rate of 1.43GHz and 4GB of low-power DDR4 Memory.
Train a custom yolo object detection model in PyTorch
Use and create YOLOV3 models with keras-yolo3.
Use YOLOv3 with OpenCV to detect objects in both images and videos.
Gilbert Tanner is a Robotics, Systems and Control student at ETH Zürich.
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