NVIDIA Jetson Object detection with YOLOv3-tiny-416

more info
http://microcontrollerkits.blogspot.c...

YOLO-v3-tiny-416
Image ( 768x576) : 22.30 FPS.
Video ( 960x540) : 20.58 FPS.

Demos showcase how to convert pre-trained yolov3 and yolov4 models through ONNX to TensorRT engines. The code for these 2 demos has gone through some significant changes. More specifically, I have recently updated the implementation with a "yolo_layer" plugin to speed up the inference time of the yolov3/yolov4 models.

What is YOLO object detector?
When it comes to deep learning-based object detection, there are three primary object detectors you’ll encounter:

R-CNN and their variants, including the original R-CNN, Fast R- CNN, and Faster R-CNN
Single Shot Detector (SSDs)
YOLO
First introduced in 2015 by Redmon et al., their paper, You Only Look Once: Unified, Real-Time Object Detection, details an object detector capable of super real-time object detection, obtaining 45 FPS on a GPU.

You Only Look Once: Unified, Real-Time Object Detection
https://arxiv.org/pdf/1506.02640v3.pdf

YOLOv3
YOLOv3 improved on the YOLOv2 paper and both Joseph Redmon and Ali Farhadi, the original authors, contributed.
Together they published YOLOv3: An Incremental Improvement
The original YOLO papers were being hosted here
Author: Joseph Redmon and Ali Farhadi
Released: 8 Apr 2018

We’ll be using YOLOv3 , YOLOv4 in this blog post, in particular, YOLO trained on the COCO dataset.
The COCO dataset consists of 80 labels.

TensorRT demos
https://github.com/jkjung-avt/tensorr...

You Only Look Once: Unified, Real-Time Object Detection
https://arxiv.org/pdf/1506.02640v3.pdf

YOLOv3: An Incremental Improvement
https://arxiv.org/pdf/1804.02767.pdf

YOLOv4: Optimal Speed and Accuracy of Object Detection
https://arxiv.org/pdf/2004.10934.pdf

DarkNet YOLO
https://github.com/AlexeyAB/darknet

YOLO Object Detection
https://pyimagesearch.com/2018/11/12/...

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