Скачать с ютуб NVIDIA Jetson Object detection with YOLOv3-tiny-416 в хорошем качестве

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/... สอบถาม เพิ่มเติม : อดุลย์ นันทะแก้ว 081-6452400 LINE : adunnan Page :   / softpowergroup   FaceBook :   / adun.nantakaew   email : [email protected]