YOLOv4

Introduced by Bochkovskiy et al. in YOLOv4: Optimal Speed and Accuracy of Object Detection

YOLOv4 is a one-stage object detection model that improves on YOLOv3 with several bags of tricks and modules introduced in the literature. The components section below details the tricks and modules used.

Source: YOLOv4: Optimal Speed and Accuracy of Object Detection

Latest Papers

PAPER DATE
Scaled-YOLOv4: Scaling Cross Stage Partial Network
| Chien-Yao WangAlexey BochkovskiyHong-Yuan Mark Liao
2020-11-16
Real-Time Polyp Detection, Localisation and Segmentation in Colonoscopy Using Deep Learning
Debesh JhaSharib AliHåvard D. JohansenDag D. JohansenJens RittscherMichael A. RieglerPål Halvorsen
2020-11-15
Real-time object detection method based on improved YOLOv4-tiny
Zicong JiangLiquan ZhaoShuaiyang LiYanfei Jia
2020-11-09
Detecting soccer balls with reduced neural networks: a comparison of multiple architectures under constrained hardware scenarios
Douglas De Rizzo MeneghettiThiago Pedro Donadon HomemJonas Henrique Renolfi de OliveiraIsaac Jesus da SilvaDanilo Hernani PericoReinaldo Augusto da Costa Bianchi
2020-09-28
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
Yuxuan CaiHongjia LiGeng YuanWei NiuYanyu LiXulong TangBin RenYanzhi Wang
2020-09-12
Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis
Vishal MandalYaw Adu-Gyamfi
2020-07-31
PP-YOLO: An Effective and Efficient Implementation of Object Detector
| Xiang LongKaipeng DengGuanzhong WangYang ZhangQingqing DangYuan GaoHui ShenJianguo RenShumin HanErrui DingShilei Wen
2020-07-23
YOLOv4: Optimal Speed and Accuracy of Object Detection
| Alexey BochkovskiyChien-Yao WangHong-Yuan Mark Liao
2020-04-23

Tasks

TASK PAPERS SHARE
Object Detection 7 63.64%
Real-Time Object Detection 3 27.27%
adversarial training 1 9.09%

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