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yolov5实现人群计数

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本项目是一个使用 YOLOv5 模型实现的人群计数 Python 应用。YOLOv5 是一个流行的目标检测模型,以其速度快和准确性高而闻名。通过这个项目,你可以快速部署一个能够识别图像中人数的系统。 功能特点: 高精度人群计数:利用 YOLOv5 模型的高效目标检测能力,实现对人群的精确计数。 实时图像处理:支持从摄像头或视频文件中实时读取图像,并进行人群计数。 易于集成:代码结构清晰,易于与其他系统或应用集成。 跨平台支持:兼容主流操作系统,包括 Windows、Linux 和 macOS。 技术栈: Python:编程语言。 YOLOv5:目标检测模型。 OpenCV:用于图像处理和显示。

[涓枃](https://docs.ultralytics.com/zh) | [頃滉淡鞏碷(https://docs.ultralytics.com/ko) | [鏃ユ湰瑾瀅(https://docs.ultralytics.com/ja) | [袪褍褋褋泻懈泄](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Fran莽ais](https://docs.ultralytics.com/fr) | [Espa帽ol](https://docs.ultralytics.com/es) | [Portugu锚s](https://docs.ultralytics.com/pt) | [T眉rk莽e](https://docs.ultralytics.com/tr) | [Ti岷縩g Vi峄噒](https://docs.ultralytics.com/vi) | [丕賱毓乇亘賷丞](https://docs.ultralytics.com/ar)
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YOLOv5 馃殌 is the world's most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development. We hope that the resources here will help you get the most out of YOLOv5. Please browse the YOLOv5 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions! To request an Enterprise License please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).
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##
YOLOv8 馃殌 NEW
We are thrilled to announce the launch of Ultralytics YOLOv8 馃殌, our NEW cutting-edge, state-of-the-art (SOTA) model released at **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image classification tasks. See the [YOLOv8 Docs](https://docs.ultralytics.com/) for details and get started with: [![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics) ```bash pip install ultralytics ``` ##
Documentation
See the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5/) for full documentation on training, testing and deployment. See below for quickstart examples.
Install Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). ```bash git clone https://github.com/ultralytics/yolov5 # clone cd yolov5 pip install -r requirements.txt # install ```
Inference YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). ```python import torch # Model model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, custom # Images img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list # Inference results = model(img) # Results results.print() # or .show(), .save(), .crop(), .pandas(), etc. ```
Inference with detect.py `detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. ```bash python detect.py --weights yolov5s.pt --source 0 # webcam img.jpg # image vid.mp4 # video scre

资源文件列表:

yolov5.zip 大约有266个文件
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  163. yolov5/models/hub/yolov5x6.yaml 1.79KB
  164. yolov5/models/segment/
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