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[2007.12099] PP-YOLO: An Effective and Efficient Implementation of Object Detector
- DOI: https://doi.org/10.48550/arXiv.2007.12099
- Authors: Long, Xiang, Deng, Kaipeng, Wang, Guanzhong…
- Published: 2020/07/23
- RelatedEntitiesURL: http://arxiv.org/pdf/2007.12099.pdf
- Subject: Computer Vision and Pattern Recognition (cs.CV)
- Cite As: arXiv:2007.12099 [cs.CV]
Object detection is one of the most important areas in computer vision, which plays a key role in various practical scenarios. Due to limitation of hardware, it is often necessary to sacrifice accuracy to ensure the infer speed of the detector in practice. Therefore, the balance between effectivenes...Explore further
[2007.12099] PP-YOLO: An Effective and Efficient …
WEBMar 14, 2024 · PP-YOLO is faster (FPS) and more accurate(COCO mAP) than other state-of-the-art detectors, such as EfficientDet and YOLOv4. In this paper, we explore a lot of tricks and show how to combine these tricks on the …
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WEBBy 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 detectors such as EfficientDet and YOLOv4. Source code is at https://github.com/PaddlePaddle/ PaddleDetection. 1. Introduction.
A Comprehensive Review of YOLO Architectures in Computer …
WEBYOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO’s evolution, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with Transformers.
PP-YOLO: An Effective and Efficient Implementation of Object …
WEB17 rows · Jul 23, 2020 · By combining multiple tricks, PP-YOLO can achieve a better …
See all 17 rows on paperswithcode.comMODEL METRIC NAME METRIC VALUE GLOBAL RANK PP-YOLO (608x608) box mAP 45.2 # 134 PP-YOLO (608x608) AP50 65.2 # 74 PP-YOLO (608x608) AP75 49.9 # 78 PP-YOLO (608x608) APS 26.3 # 84
[2004.10934] YOLOv4: Optimal Speed and Accuracy of Object …
WEBApr 23, 2020 · YOLOv4: Optimal Speed and Accuracy of Object Detection. Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao. (Submitted on 23 Apr 2020) There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy.
- Studies of pp yolov4
PP-YOLO Surpasses YOLOv4 - State of the Art …
WEBAug 3, 2020 · The final PP-YOLO model improves the mAP on COCO from 43.5% to 45.2% at a speed faster than YOLOv4 (emphasis ours) The PP-YOLO contributions reference above took the YOLOv3 model from 38.9 …
YOLOv4 Explained | Papers With Code
WEBYOLOv4 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. Read Paper See Code. Papers. Tasks. Usage Over Time.
PP-YOLO: An Effective and Efficient Implementation of Object …
WEBJul 22, 2020 · ArXiv. SJR Quartile - UNKNOWN. Semantic Scholar. Key takeaway. PP-YOLO, a new object detector based on YOLOv3, achieves a better balance between effectiveness (45.2% mAP) and efficiency (72.9 FPS) than existing state-of-the-art detectors like EfficientDet and YOLOv4. Abstract.
Review — PP-YOLO: An Effective and Efficient …
WEB5 min read. ·. Aug 18, 2022. PP-YOLO runs faster than YOLOv4 and improves mAP from 43.5% to 45.2%. PP-YOLO: An Effective and Efficient Implementation of Object Detector. PP-YOLO, by...
YOLO v4 or YOLO v5 or PP-YOLO? - Towards Data …
WEBAug 22, 2020. 5. O bject detection is a computer vision task that involves predicting the presence of one or more objects, along with their classes and bounding boxes. YOLO (You Only Look Once) is a state of art Object …
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.% mAP) and efficiency (72.9 FPS), surpassing the existing state-of-the-art detectors such as...
PP-YOLO Explained | Papers With Code
WEBPP-YOLO is an object detector based on YOLOv3. It mainly tries to combine various existing tricks that almost not increase the number of model parameters and FLOPs, to achieve the goal of improving the accuracy of detector as much as possible while ensuring that the speed is almost unchanged.
YOLOv4‐dense: A smaller and faster YOLOv4 for …
WEBNov 8, 2022 · To overcome these difficulties, in this work a novel YOLOv4-dense model is proposed to detect objects in an accurate, fast manner, which is built on top of the YOLOv4 framework but with substantial …
Introduction to the YOLO Family - PyImageSearch
WEBApr 4, 2022 · Introduction to Object Detection. Challenges. History of Object Detection. What Are Single-Stage Object Detectors? YOLOv1. YOLOv2. YOLOv3. YOLOv4. YOLOv5. Mosaic Data Augmentation. Quantitative Benchmark. YOLOv5 Nano Release. YOLOv5n Compared with YOLOv4-Tiny. PP-YOLO. PaddleDetection. PP-YOLO Performance. PP …
WEBThe YOLOv4-tiny model achieves 22.0% AP (42.0% AP50) at a speed of ∼443 FPS on RTX 2080Ti, while by using Ten-sorRT, batch size = 4 and FP16-precision the YOLOv4-tiny achieves 1774 FPS. 1. Introduction. The deep learning-based object detection technique has many applications in our daily life.
YOLOv4: Optimal Speed and Accuracy of Object Detection
WEBApr 23, 2020 · 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 combinations of such features on large datasets, and theoretical justification of the result, is required.
What’s new in YOLOv4? - Towards Data Science
WEBMay 19, 2020. 2. YOLO detecting objects in a video in real-time. What is YOLO? YOLO is short for You Only Look Once. It is a real-time object recognition system that can recognize multiple objects in a single frame. YOLO recognizes objects more precisely and faster than other recognition systems.
WEBYOLOv4 PP-YOLO YOLOv5 YOLOv6 YOLOX YOLOR PP-YOLOv2 DAMO YOLO PP-YOLOE Y OL v7 YOLOv6 2015 2016 2018 2020 2022 2023 YOLOv8 Figure 1: A timeline of YOLO versions. 2 YOLO Applications Across Diverse Fields YOLO’s real-time object detection capabilities have been invaluable in autonomous vehicle systems, enabling quick
Keypoint regression strategy and angle loss based YOLO for …
WEBNov 17, 2023 · This paper proposes an enhanced version of YOLOv4 called KR–AL–YOLO (keypoint regression strategy and angle loss based YOLOv4). The KR–AL–YOLO approach introduces two customized modules:...
A Lightweight and Accurate UAV Detection Method Based on …
WEBSep 11, 2022 · This paper proposes an accurate and lightweight UAV detection model based on YOLOv4. To verify the effectiveness of this model, we made a UAV dataset, which contains four types of UAVs and 20,365 images.
WEBYOLOv4 runs twice faster than EfficientDet with comparable performance. Improves YOLOv3’s AP and FPS by 10% and 12%, respectively.
Applied Sciences | Free Full-Text | A Review of Deep Learning
WEB9 hours ago · The modified YOLOv4 model demonstrates superior mean average precision (mAP) compared to the original YOLOv4, with a notable increase of 5.29 % (on the dataset from ... Cairo, Egypt, 27–28 December 2011; pp. 72–82. [Google Scholar] Ziebinski, A.; Cupek, R.; Erdogan, H.; Waechter, S. A survey of ADAS technologies for the future …
Enhancement of Outdated Vision Systems in SMEs with
WEB5 days ago · One of the challenges with old machine vision systems is appearance of the pseudo failures, as well as substantially long processing times. However, the use of YOLOv4 solution to enhance the old vision system accuracy resulted in reduced pseudo failures from 10% to 3%, thus making significant financial savings.
[2104.10419] PP-YOLOv2: A Practical Object Detector - arXiv.org
WEBApr 21, 2021 · Since 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 engine with TensorRT, FP16-precision, and batch size = 1 further improves PP-YOLOv2's infer speed, which achieves 106.5 FPS.
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