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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.orgarXiv.org e-Print archive
Explore a wide range of e-prints on the arXiv.org archive, including papers on …
- 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
(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 …
- Estimated Reading Time: 3 mins
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 …
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.
PP-YOLO: An Effective and Efficient Implementation of Object …
WEBThis paper and YOLOv4 both starts from YOLOv3 but adopts different methods. YOLOv4 explores extensively recent advances in backbones and data augmentation, while PP …
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 …
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 …
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 …
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 …
[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 …
(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 …
(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 …
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 …
arXiv.org e-Print archive
WEBExplore a wide range of e-prints on the arXiv.org archive, including papers on YOLO object detection and its various architectures.
[PDF] PP-YOLOE: An evolved version of YOLO - Semantic Scholar
WEBMar 30, 2022 · PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment is presented, using anchor-free paradigm, more …
PP-YOLO: An Effective and Efficient Implementation of Object …
WEBJul 23, 2020 · By combining multiple tricks, PP-YOLO can achieve a better balance between effectiveness (45.2% mAP) and efficiency (72.9 FPS), surpassing the existing …
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. …
Brain tumor detection and segmentation using deep learning
WEB3 days ago · Initially, the performance of three state-of-the-art deep learning-based object detection networks i.e. Faster R-CNN , YOLO version 4 (YOLOv4) , YOLO version 5 …
WEB1. Introduction. real-time applications. They extract the most advanced detection technologies available at the time (e.g., anchors [26] for YOLOv2 [24], Residual Net [9] …
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