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YOLOv4: Optimal Speed and Accuracy of Object Detection
Apr 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 of features …
- 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 …
- The majority of CNN-based object detectors are largely applicable only for recommendation systems. For example, searching for free parking spaces via urban video cameras is executed by slow accurate models, whereas car collision warning is related to fast inaccurate models. Improving the real-time object detector accuracy enables using them not onl...
- Author: Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao
- Publish Year: 2020
YOLOv4: Optimal Speed and Accuracy of Object …
Apr 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 state-of-the-art results: …
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(PDF) YOLOv4: Optimal Speed and Accuracy of …
Apr 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%...
per 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 efficiency (72.9 …
[2004.10934] YOLOv4: Optimal Speed and Accuracy of Object …
Apr 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
YOLOv4: Optimal Speed and Accuracy of Object Detection. There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. Practical testing of …
YOLOv4: Optimal Speed and Accuracy of Object …
We 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 (65.7% AP50) for …
YOLOv4: Optimal Speed and Accuracy of Object …
The 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.
[PDF] PP-YOLO: An Effective and Efficient …
Jul 23, 2020 · A new enhanced YOLOv4-based model is proposed that outperforms the accuracy of YOLov4 without considerably compromising its speed and several optimization techniques are explored, mainly grouped, …
(PDF) YOLOv4‐dense: A smaller and faster YOLOv4 for …
Nov 8, 2022 · Experimental results on NVIDIA JETSON TX2 embedded platform demonstrate that YOLOv4‐dense can achieve a higher accuracy, faster speed with smaller model size.
Improves 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-mization …
[PDF] Scaled-YOLOv4: Scaling Cross Stage Partial Network
Nov 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 and down and is …
lowing 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 efficient, called …
1. 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 deployed on arti …
(PDF) PP-YOLO: An Effective and Efficient Implementation
Jul 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 state-of-the-art …
We 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-taining optimal …
The 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. Following this, …
(PDF) Scaled-YOLOv4: Scaling Cross Stage Partial Network
Jun 1, 2021 · Architecture of YOLOv4-large, including YOLOv4-P5, YOLOv4-P6, and YOLOv4-P7. The dashed arrow means replace the corresponding CSPUp block by CSPSPP block. …
PP-YOLO: An Effective and Efficient Implementation of Object …
Jul 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 state-of-the-art …
arXiv.org e-Print archive
Explore 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
Mar 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 powerful backbone …
(PDF) PP-YOLOv2: A Practical Object Detector - ResearchGate
Apr 21, 2021 · This paper will analyze a collection of refinements and empirically evaluate their impact on the final model performance through incremental ablation study. Things we tried that …
Detection of Acute Myeloid Leukemia Using Deep Learning
2 days ago · Very recently, Luong et al. used YOLOv8 for blood cell classification and achieved an accuracy of 95% [15]. In this research, deep learning model-based systems using YOLOv4 …
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