YoloV7-ncnn-Jetson-Nano and NanoDet-ncnn-Jetson-Nano
These are competitor object detection models, YOLOv7 and NanoDet, both optimized by Qengineering for deployment on a Jetson Nano using the ncnn neural network inference framework.
About YoloV7-ncnn-Jetson-Nano
Qengineering/YoloV7-ncnn-Jetson-Nano
YoloV7 for a Jetson Nano using ncnn.
This project helps operations engineers and robotics enthusiasts perform real-time object detection on embedded systems. It takes video streams or images as input and outputs bounding boxes around detected objects, identifying what they are. This is ideal for scenarios requiring immediate analysis on devices like security cameras, drones, or automated vehicles.
About NanoDet-ncnn-Jetson-Nano
Qengineering/NanoDet-ncnn-Jetson-Nano
NanoDet for Jetson Nano
This project helps developers and engineers implement real-time object detection on embedded devices. It takes in live video streams or images and identifies objects within them, such as cars or people, with high speed. This is useful for robotics engineers, smart city developers, or anyone building embedded vision applications.
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