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Title: YOLOv4: Optimal Speed and Accuracy of Object Detection
WEBApr 23, 2020 · View a PDF of the paper titled YOLOv4: Optimal Speed and Accuracy of Object Detection, by Alexey Bochkovskiy and 2 other authors. There are a huge number …
- Author: Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao
- Cite as: arXiv:2004.10934 [cs.CV]
- Publish Year: 2020
See results only from arxiv.orgPP-YOLOv2: A Practical Obj…
Since a significant margin of performance has been made, we present PP …
WEBper are conducted based on PaddlePaddle, we call it PP-YOLO. By combining multiple tricks, PP-YOLO can achieve a better balance between effectiveness (45.2% mAP) and …
- Bag of Freebies (BoF) for backbone: CutMix and Mosaic data augmentation, DropBlock regularization, Class label smoothing Bag of Specials (BoS) for backbone: Mish activa-tion, Cross-stage partial connections (CSP), Multi-input weighted residual connections (MiWRC) Bag of Freebies (BoF) for detector: CIoU-loss, CmBN, DropBlock regularization, Mosaic ...
- Author: Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao
- Publish Year: 2020
- Studies of pp yolov4 pdf
(PDF) YOLOv4: Optimal Speed and Accuracy of …
WEBApr 22, 2020 · Comparison of the proposed YOLOv4 and other state-of-the-art object detectors. YOLOv4 runs twice faster than EfficientDet with comparable performance. Improves YOLOv3's AP and FPS by 10%...
YOLOv4: Optimal Speed and Accuracy of Object …
WEBApr 23, 2020 · TLDR. This work uses new features: WRC, CSP, CmBN, SAT, Mish activation, Mosaic data augmentation, C mBN, DropBlock regularization, and CIoU loss, and combine some of them to achieve …
Title: YOLOv4: Optimal Speed and Accuracy of Object Detection
WEBApr 23, 2020 · A paper that presents a new object detection model, YOLOv4, with various features and techniques to improve CNN accuracy and speed. The paper is not in PDF …
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YOLOv4: Optimal Speed and Accuracy of Object Detection
WEBYOLOv4: Optimal Speed and Accuracy of Object Detection. There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. …
YOLOv4: Optimal Speed and Accuracy of Object …
WEBWe use new features: WRC, CSP, CmBN, SAT, Mish activation, Mosaic data augmentation, CmBN, DropBlock regularization, and CIoU loss, and combine some of them to achieve state-of-the-art results: 43.5% AP …
YOLOv4: Optimal Speed and Accuracy of Object …
WEBThe aim of this paper is to improve the newly released, YOLOv4 detector, specifically, for vehicle tracking applications using some existing methods such as optimising anchor box predictions by using k-means clustering.
ultralytics/docs/en/models/yolov4.md at main - GitHub
WEBYOLOv4, which stands for "You Only Look Once version 4," is a state-of-the-art real-time object detection model developed by Alexey Bochkovskiy in 2020. It achieves an optimal …
(PDF) YOLOv4‐dense: A smaller and faster YOLOv4 for …
WEBNov 8, 2022 · Experimental results on NVIDIA JETSON TX2 embedded platform demonstrate that YOLOv4‐dense can achieve a higher accuracy, faster speed with …
WEBlowing YOLOv4 and YOLOv5. We enhance the neck with RepBlocks or CSPStackRep Blocks to have Rep-PAN. Head: We simplify the decoupled head to make it more …
WEB1. Introduction. Low power computer vision is one of the most con- cerned topics of academia and industry today, mainly be- cause nowadays there are many applications …
WEBImproves YOLOv3’s AP and FPS by 10% and 12%, respectively. The main goal of this work is designing a fast operating speed of an object detector in production systems and opti …
[PDF] PP-YOLO: An Effective and Efficient Implementation of …
WEBA new enhanced YOLOv4-based model is proposed that outperforms the accuracy of YOLov4 without considerably compromising its speed and several optimization …
WEBWe show that the YOLOv4 object detection neural net-work based on the CSP approach, scales both up and down and is applicable to small and large networks while main …
(PDF) Scaled-YOLOv4: Scaling Cross Stage Partial Network
WEBJun 1, 2021 · Architecture of YOLOv4-large, including YOLOv4-P5, YOLOv4-P6, and YOLOv4-P7. The dashed arrow means replace the corresponding CSPUp block by …
[PDF] Scaled-YOLOv4: Scaling Cross Stage Partial Network
WEBNov 16, 2020 · Scaled-YOLOv4: Scaling Cross Stage Partial Network. We show that the YOLOv4 object detection neural network based on the CSP approach, scales both up …
PP-YOLO Surpasses YOLOv4 - State of the Art Object Detection …
WEBAug 3, 2020 · PP-YOLO Surpasses YOLOv4 - State of the Art Object Detection Techniques. Baidu publishes PP-YOLO and pushes the state of the art in object …
WEBThe paper begins by exploring the foundational concepts and architecture of the original YOLO model, which set the stage for the subsequent advances in the YOLO family. …
(PDF) PP-YOLO: An Effective and Efficient Implementation
WEBJul 23, 2020 · By combining multiple tricks, PP-YOLO can achieve a better balance between effectiveness (45.% mAP) and efficiency (72.9 FPS), surpassing the existing …
CVPR 2021 Open Access Repository
WEBScaled-YOLOv4: Scaling Cross Stage Partial Network. We show that the YOLOv4 object detection neural network based on the CSP approach, scales both up and down and is …
WEBSince a significant margin of performance has been made, we present PP-YOLOv2. In terms of speed, PP-YOLOv2 runs in 68.9FPS at 640x640 input size. Paddle inference …
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