NVIDIA Jetson Xavier YOLOv4 CrowdHuman Model
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CrowdHuman is a benchmark dataset to better evaluate detectors in crowd scenarios. The CrowdHuman dataset is large, rich-annotated and contains high diversity. CrowdHuman contains 15000, 4370 and 5000 images for training, validation, and testing, respectively. There are a total of 470K human instances from train and validation subsets and 23 persons per image, with various kinds of occlusions in the dataset. Each human instance is annotated with a head bounding-box, human visible-region bounding-box and human full-body bounding-box. We hope our dataset will serve as a solid baseline and help promote future research in human detection tasks.
What is a 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
YOLOv4
With the original authors work on YOLO coming to a standstill, YOLOv4 was released by Alexey Bochoknovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. The paper was titled YOLOv4: Optimal Speed and Accuracy of Object Detection
Author: Alexey Bochoknovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao
Released: 23 April 2020
Reference
CrownHuman Dataset https://www.crowdhuman.org/
DarkNet YOLO https://github.com/AlexeyAB/darknet
TensorRT YOLO For Custom Trained Models
https://jkjung-avt.github.io/trt-yolo...
YOLOv4 CrowdHuman Tutorial https://github.com/jkjung-avt/yolov4_...
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