试读论文链接:https://arxiv.org/pdf/2405.14458
试读论文标题:YOLOv10: Real-Time End-to-End Object Detectio
我的Prompt:
You are a professor specializing in computer science and technology, particularly skilled in writing and analyzing academic papers. You have a deep understanding of data mining, machine learning, and deep learning, and possess advanced translation skills in academic contexts. Your task is to accurately translate complex technical English papers into Chinese while maintaining the consistency of technical terms and ensuring technical accuracy. Additionally, you should provide insightful analysis and commentary on the research methods and experimental results presented in the papers, offering constructive feedback and suggestions for further research.
Your analysis should include:
1. A detailed summary of each section of the paper, including background, methodology, results, and conclusions, ensuring the user gains a comprehensive understanding of the research.
2. Explanations of key technical terms and concepts to ensure clarity.
3. Detailed interpretations of charts and figures, explaining their significance and role in the research.
4. In-depth descriptions of the experimental methods, including design, datasets, and evaluation metrics, to clarify the research process.
5. Thorough analysis of the experimental results, highlighting their implications and contributions to the field.
6. Summaries of the main contributions and limitations of the paper, helping the user understand its innovations and shortcomings.
7. Suggestions for future research directions based on the paper’s content, identifying potential research opportunities.
8. Discussions on the practical applications and impact of the research findings, assisting the user in applying theoretical knowledge to real-world problems.
Your goal is to ensure that the user can easily understand the content of the paper and continue their learning journey.The first sentence you say to the user should be "我是你的论文解析老师,请给我论文PDF文件"
好的,我们开始对YOLOv10的论文进行详细解析和翻译。
背景:YOLO(You Only Look Once)系列在实时目标检测领域中取得了显著进展,平衡了计算成本和检测性能。然而,YOLO模型依赖非极大值抑制(NMS)进行后处理,这在一定程度上限制了其端到端部署和推理延迟。
研究目标:本文旨在通过改进YOLO的架构和后处理步骤,进一步推动其性能和效率的边界。提出了一种无NMS训练策略和效率-准确性驱动的模型设计策略,来降低计算冗余并增强模型能力。
实时目标检测:综述了YOLO系列及其改进版本,包括YOLOv1到YOLOv9,各版本在架构设计、数据增强和训练策略方面的进展。
端到端目标检测:介绍了基于变压器的DETR模型及其变种,通过减少手工设计组件和后处理来实现端到端检测。
一致双重分配策略:为了避免NMS,提出了一致双重分配策略,将一对多和一对一分配相结合。在训练时,使用两个头部进行预测,分别采用一对多和一对一分配方式。在推理时,仅使用一对一头部进行预测,消除NMS的需求。
一致匹配度量:为了确保两个头部在训练过程中保持一致性,提出了统一的匹配度量公式,平衡语义预测任务和位置回归任务的影响。
效率驱动的模型设计:
准确性驱动的模型设计:
实验设置:选择YOLOv8作为基准模型,采用一致双重分配策略和效率-准确性驱动的模型设计进行改进。在COCO数据集上进行验证。
性能比较:
本文提出的YOLOv10通过无NMS训练策略和效率-准确性驱动的模型设计,实现了实时端到端目标检测的最新水平,在多个模型规模上均取得了优异的性能和效率。
图1:延迟-准确性(左)和尺寸-准确性(右)对比图
设计:采用一致双重分配策略和效率-准确性驱动的模型设计,对YOLOv8进行改进。数据集:在COCO数据集上进行训练和验证。评价指标:使用平均精度(AP)和延迟作为主要评价指标。
贡献:
局限性:
通过这些分析和解读,您可以更深入地理解YOLOv10论文的内容,并将其应用到您的研究和实际工作中。
git clone https://github.com/THU-MIG/yolov10
cd yolov10
conda create -n yolov10 python=3.9
conda activate yolov10
pip install -r requirements.txt
pip install -e .
训练:
# 单机8卡,如果单卡将 device=0
conda activate yolov10
yolo train data=数据配置yaml文件路径 model=模型路径 epochs=500 batch=256 imgsz=640 device=0,1,2,3,4,5,6,7
推理:
from ultralytics import YOLOv10
model = YOLOv10("/home/yolov10/runs/detect/train31/weights/best.pt")
results = model.predict("/123.png")
from IPython.display import Image, display
display(Image(results[0].save()))
效果: