ZIPyolov5实现人群计数 30.58MB

qq_36852840

资源文件列表:

yolov5.zip 大约有266个文件
  1. yolov5/
  2. yolov5/.dockerignore 3.61KB
  3. yolov5/.git/
  4. yolov5/.git/branches/
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  7. yolov5/.git/FETCH_HEAD
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  24. yolov5/.git/info/
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  28. yolov5/.git/logs/refs/
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  35. yolov5/.git/objects/
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  71. yolov5/.git/refs/
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  74. yolov5/.git/refs/remotes/
  75. yolov5/.git/refs/remotes/origin/
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  78. yolov5/.git/refs/tags/
  79. yolov5/.gitattributes 75B
  80. yolov5/.github/
  81. yolov5/.github/dependabot.yml 649B
  82. yolov5/.github/ISSUE_TEMPLATE/
  83. yolov5/.github/ISSUE_TEMPLATE/bug-report.yml 2.9KB
  84. yolov5/.github/ISSUE_TEMPLATE/config.yml 405B
  85. yolov5/.github/ISSUE_TEMPLATE/feature-request.yml 1.79KB
  86. yolov5/.github/ISSUE_TEMPLATE/question.yml 1.17KB
  87. yolov5/.github/workflows/
  88. yolov5/.github/workflows/ci-testing.yml 6.8KB
  89. yolov5/.github/workflows/cla.yml 1.57KB
  90. yolov5/.github/workflows/codeql-analysis.yml 1.17KB
  91. yolov5/.github/workflows/docker.yml 1.66KB
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  93. yolov5/.github/workflows/links.yml 2.74KB
  94. yolov5/.github/workflows/merge-main-into-prs.yml 3.02KB
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  96. yolov5/.gitignore 3.92KB
  97. yolov5/benchmarks.py 13.79KB
  98. yolov5/CITATION.cff 393B
  99. yolov5/classify/
  100. yolov5/classify/predict.py 11.81KB
  101. yolov5/classify/train.py 16.1KB
  102. yolov5/classify/tutorial.ipynb 93.67KB
  103. yolov5/classify/val.py 8.01KB
  104. yolov5/CONTRIBUTING.md 4.89KB
  105. yolov5/data/
  106. yolov5/data/Argoverse.yaml 2.66KB
  107. yolov5/data/coco.yaml 2.43KB
  108. yolov5/data/coco128-seg.yaml 1.85KB
  109. yolov5/data/coco128.yaml 1.83KB
  110. yolov5/data/GlobalWheat2020.yaml 1.84KB
  111. yolov5/data/hyps/
  112. yolov5/data/hyps/hyp.no-augmentation.yaml 1.61KB
  113. yolov5/data/hyps/hyp.Objects365.yaml 671B
  114. yolov5/data/hyps/hyp.scratch-high.yaml 1.61KB
  115. yolov5/data/hyps/hyp.scratch-low.yaml 1.62KB
  116. yolov5/data/hyps/hyp.scratch-med.yaml 1.62KB
  117. yolov5/data/hyps/hyp.VOC.yaml 1.13KB
  118. yolov5/data/ImageNet.yaml 18.42KB
  119. yolov5/data/ImageNet10.yaml 929B
  120. yolov5/data/ImageNet100.yaml 2.58KB
  121. yolov5/data/ImageNet1000.yaml 18.42KB
  122. yolov5/data/images/
  123. yolov5/data/images/bus.jpg 476.01KB
  124. yolov5/data/images/zidane.jpg 164.99KB
  125. yolov5/data/Objects365.yaml 8.98KB
  126. yolov5/data/scripts/
  127. yolov5/data/scripts/download_weights.sh 641B
  128. yolov5/data/scripts/get_coco.sh 1.53KB
  129. yolov5/data/scripts/get_coco128.sh 619B
  130. yolov5/data/scripts/get_imagenet.sh 1.63KB
  131. yolov5/data/scripts/get_imagenet10.sh 734B
  132. yolov5/data/scripts/get_imagenet100.sh 738B
  133. yolov5/data/scripts/get_imagenet1000.sh 742B
  134. yolov5/data/SKU-110K.yaml 2.28KB
  135. yolov5/data/VisDrone.yaml 2.9KB
  136. yolov5/data/VOC.yaml 3.41KB
  137. yolov5/data/xView.yaml 5.04KB
  138. yolov5/export.py 66.14KB
  139. yolov5/hubconf.py 23.41KB
  140. yolov5/LICENSE 33.71KB
  141. yolov5/models/
  142. yolov5/models/common.py 50.89KB
  143. yolov5/models/experimental.py 5.03KB
  144. yolov5/models/hub/
  145. yolov5/models/hub/anchors.yaml 3.26KB
  146. yolov5/models/hub/yolov3-spp.yaml 1.55KB
  147. yolov5/models/hub/yolov3-tiny.yaml 1.22KB
  148. yolov5/models/hub/yolov3.yaml 1.54KB
  149. yolov5/models/hub/yolov5-bifpn.yaml 1.41KB
  150. yolov5/models/hub/yolov5-fpn.yaml 1.2KB
  151. yolov5/models/hub/yolov5-p2.yaml 1.66KB
  152. yolov5/models/hub/yolov5-p34.yaml 1.21KB
  153. yolov5/models/hub/yolov5-p6.yaml 1.71KB
  154. yolov5/models/hub/yolov5-p7.yaml 2.09KB
  155. yolov5/models/hub/yolov5-panet.yaml 1.39KB
  156. yolov5/models/hub/yolov5l6.yaml 1.79KB
  157. yolov5/models/hub/yolov5m6.yaml 1.79KB
  158. yolov5/models/hub/yolov5n6.yaml 1.79KB
  159. yolov5/models/hub/yolov5s-ghost.yaml 1.46KB
  160. yolov5/models/hub/yolov5s-LeakyReLU.yaml 1.48KB
  161. yolov5/models/hub/yolov5s-transformer.yaml 1.42KB
  162. yolov5/models/hub/yolov5s6.yaml 1.79KB
  163. yolov5/models/hub/yolov5x6.yaml 1.79KB
  164. yolov5/models/segment/
  165. yolov5/models/segment/yolov5l-seg.yaml 1.39KB
  166. yolov5/models/segment/yolov5m-seg.yaml 1.4KB
  167. yolov5/models/segment/yolov5n-seg.yaml 1.4KB
  168. yolov5/models/segment/yolov5s-seg.yaml 1.4KB
  169. yolov5/models/segment/yolov5x-seg.yaml 1.4KB
  170. yolov5/models/tf.py 32.97KB
  171. yolov5/models/yolo.py 20.54KB
  172. yolov5/models/yolov5l.yaml 1.38KB
  173. yolov5/models/yolov5m.yaml 1.39KB
  174. yolov5/models/yolov5n.yaml 1.39KB
  175. yolov5/models/yolov5s.yaml 1.39KB
  176. yolov5/models/yolov5x.yaml 1.39KB
  177. yolov5/models/__init__.py
  178. yolov5/models/__pycache__/
  179. yolov5/models/__pycache__/common.cpython-38.pyc 48.04KB
  180. yolov5/models/__pycache__/experimental.cpython-38.pyc 5.83KB
  181. yolov5/models/__pycache__/yolo.cpython-38.pyc 19.27KB
  182. yolov5/models/__pycache__/__init__.cpython-38.pyc 164B
  183. yolov5/person_count.py 23.52KB
  184. yolov5/pyproject.toml 5.25KB
  185. yolov5/README.md 42.15KB
  186. yolov5/README.zh-CN.md 42.25KB
  187. yolov5/requirements.txt 1.56KB
  188. yolov5/runs/
  189. yolov5/segment/
  190. yolov5/segment/predict.py 15.91KB
  191. yolov5/segment/train.py 34.35KB
  192. yolov5/segment/tutorial.ipynb 40.08KB
  193. yolov5/segment/val.py 23.8KB
  194. yolov5/test.jpg 427.76KB
  195. yolov5/train.py 46.03KB
  196. yolov5/tutorial.ipynb 40.45KB
  197. yolov5/utils/
  198. yolov5/utils/activations.py 4.89KB
  199. yolov5/utils/augmentations.py 18.51KB
  200. yolov5/utils/autoanchor.py 7.75KB
  201. yolov5/utils/autobatch.py 2.97KB
  202. yolov5/utils/aws/
  203. yolov5/utils/aws/mime.sh 780B
  204. yolov5/utils/aws/resume.py 1.21KB
  205. yolov5/utils/aws/userdata.sh 1.22KB
  206. yolov5/utils/aws/__init__.py
  207. yolov5/utils/callbacks.py 2.65KB
  208. yolov5/utils/dataloaders.py 59.07KB
  209. yolov5/utils/docker/
  210. yolov5/utils/docker/Dockerfile 2.5KB
  211. yolov5/utils/docker/Dockerfile-arm64 1.53KB
  212. yolov5/utils/docker/Dockerfile-cpu 1.78KB
  213. yolov5/utils/downloads.py 5.11KB
  214. yolov5/utils/flask_rest_api/
  215. yolov5/utils/flask_rest_api/example_request.py 365B
  216. yolov5/utils/flask_rest_api/README.md 1.68KB
  217. yolov5/utils/flask_rest_api/restapi.py 1.54KB
  218. yolov5/utils/general.py 50.24KB
  219. yolov5/utils/google_app_engine/
  220. yolov5/utils/google_app_engine/additional_requirements.txt 264B
  221. yolov5/utils/google_app_engine/app.yaml 219B
  222. yolov5/utils/google_app_engine/Dockerfile 821B
  223. yolov5/utils/loggers/
  224. yolov5/utils/loggers/clearml/
  225. yolov5/utils/loggers/clearml/clearml_utils.py 9.47KB
  226. yolov5/utils/loggers/clearml/hpo.py 5.17KB
  227. yolov5/utils/loggers/clearml/README.md 10.58KB
  228. yolov5/utils/loggers/clearml/__init__.py
  229. yolov5/utils/loggers/comet/
  230. yolov5/utils/loggers/comet/comet_utils.py 4.71KB
  231. yolov5/utils/loggers/comet/hpo.py 6.77KB
  232. yolov5/utils/loggers/comet/optimizer_config.json 2.37KB
  233. yolov5/utils/loggers/comet/README.md 10.56KB
  234. yolov5/utils/loggers/comet/__init__.py 21.09KB
  235. yolov5/utils/loggers/wandb/
  236. yolov5/utils/loggers/wandb/wandb_utils.py 7.95KB
  237. yolov5/utils/loggers/wandb/__init__.py
  238. yolov5/utils/loggers/__init__.py 19.78KB
  239. yolov5/utils/loss.py 11.11KB
  240. yolov5/utils/metrics.py 15.13KB
  241. yolov5/utils/plots.py 20.21KB
  242. yolov5/utils/segment/
  243. yolov5/utils/segment/augmentations.py 3.68KB
  244. yolov5/utils/segment/dataloaders.py 13.38KB
  245. yolov5/utils/segment/general.py 5.8KB
  246. yolov5/utils/segment/loss.py 9KB
  247. yolov5/utils/segment/metrics.py 5.88KB
  248. yolov5/utils/segment/plots.py 6.53KB
  249. yolov5/utils/segment/__init__.py
  250. yolov5/utils/torch_utils.py 21.15KB
  251. yolov5/utils/triton.py 3.7KB
  252. yolov5/utils/__init__.py 3.17KB
  253. yolov5/utils/__pycache__/
  254. yolov5/utils/__pycache__/augmentations.cpython-38.pyc 16.5KB
  255. yolov5/utils/__pycache__/autoanchor.cpython-38.pyc 6.82KB
  256. yolov5/utils/__pycache__/dataloaders.cpython-38.pyc 49.1KB
  257. yolov5/utils/__pycache__/downloads.cpython-38.pyc 4.76KB
  258. yolov5/utils/__pycache__/general.cpython-38.pyc 45.55KB
  259. yolov5/utils/__pycache__/metrics.cpython-38.pyc 12.28KB
  260. yolov5/utils/__pycache__/plots.cpython-38.pyc 19.54KB
  261. yolov5/utils/__pycache__/torch_utils.cpython-38.pyc 19.65KB
  262. yolov5/utils/__pycache__/__init__.cpython-38.pyc 3.61KB
  263. yolov5/val.py 29.71KB
  264. yolov5/yolov5s.pt 14.12MB
  265. yolov5/__pycache__/
  266. yolov5/__pycache__/export.cpython-38.pyc 56.73KB

资源介绍:

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。
<div align="center"> <p> <a href="https://www.ultralytics.com/events/yolovision" target="_blank"> <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a> </p> [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) <div> <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a> <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a> <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a> <a href="https://discord.com/invite/ultralytics"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a> <a href="https://reddit.com/r/ultralytics"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a> <br> <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> </div> <br> YOLOv5 馃殌 is the world's most loved vision AI, representing <a href="https://www.ultralytics.com/">Ultralytics</a> 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 <a href="https://docs.ultralytics.com/yolov5/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/yolov5/issues/new/choose">GitHub</a> for support, and join our <a href="https://discord.com/invite/ultralytics">Discord</a> community for questions and discussions! To request an Enterprise License please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license). <div align="center"> <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> <a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="2%" alt="Ultralytics BiliBili"></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%"> <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a> </div> </div> <br> ## <div align="center">YOLOv8 馃殌 NEW</div> 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 ``` <div align="center"> <a href="https://www.ultralytics.com/yolo" target="_blank"> <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a> </div> ## <div align="center">Documentation</div> See the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5/) for full documentation on training, testing and deployment. See below for quickstart examples. <details open> <summary>Install</summary> 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 ``` </details> <details> <summary>Inference</summary> 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. ``` </details> <details> <summary>Inference with detect.py</summary> `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
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