首页下载资源人工智能基于YOLOv8-Pose的姿态识别项目，带数据集可直接跑通的源码

ZIP基于YOLOv8-Pose的姿态识别项目，带数据集可直接跑通的源码

qq_4245213430.81MB需要积分:1

资源文件列表:

ultralytics-pose.zip 大约有1149个文件
  1. .github/
  2. .github/dependabot.yml 647B
  3. .github/ISSUE_TEMPLATE/
  4. .github/ISSUE_TEMPLATE/bug-report.yml 3.29KB
  5. .github/ISSUE_TEMPLATE/config.yml 363B
  6. .github/ISSUE_TEMPLATE/feature-request.yml 1.81KB
  7. .github/ISSUE_TEMPLATE/question.yml 1.19KB
  8. .github/workflows/
  9. .github/workflows/ci.yaml 10.82KB
  10. .github/workflows/cla.yml 1.39KB
  11. .github/workflows/codeql.yaml 1.17KB
  12. .github/workflows/docker.yaml 6.18KB
  13. .github/workflows/greetings.yml 4.88KB
  14. .github/workflows/links.yml 2.9KB
  15. .github/workflows/publish.yml 5.72KB
  16. .github/workflows/stale.yml 2.31KB
  17. .gitignore 2.24KB
  18. .idea/
  19. .idea/.gitignore 184B
  20. .idea/.name 10B
  21. .idea/inspectionProfiles/
  22. .idea/inspectionProfiles/profiles_settings.xml 174B
  23. .idea/inspectionProfiles/Project_Default.xml 251B
  24. .idea/misc.xml 199B
  25. .idea/modules.xml 291B
  26. .idea/ultralytics-pose.iml 611B
  27. .idea/workspace.xml 5.77KB
  28. .pre-commit-config.yaml 2.33KB
  29. CITATION.cff 612B
  30. coco8-pose/
  31. coco8-pose/images/
  32. coco8-pose/images/train/
  33. coco8-pose/images/train/000000000036.jpg 39.82KB
  34. coco8-pose/images/train/000000000049.jpg 25.09KB
  35. coco8-pose/images/train/000000000077.jpg 32.03KB
  36. coco8-pose/images/train/000000000086.jpg 59.45KB
  37. coco8-pose/images/val/
  38. coco8-pose/images/val/000000000110.jpg 35.24KB
  39. coco8-pose/images/val/000000000113.jpg 33.57KB
  40. coco8-pose/images/val/000000000136.jpg 19.79KB
  41. coco8-pose/images/val/000000000151.jpg 67.86KB
  42. coco8-pose/labels/
  43. coco8-pose/labels/train/
  44. coco8-pose/labels/train.cache 3.12KB
  45. coco8-pose/labels/train/000000000036.txt 497B
  46. coco8-pose/labels/train/000000000049.txt 994B
  47. coco8-pose/labels/train/000000000077.txt 1.46KB
  48. coco8-pose/labels/train/000000000086.txt 497B
  49. coco8-pose/labels/val/
  50. coco8-pose/labels/val.cache 4.65KB
  51. coco8-pose/labels/val/000000000110.txt 3.88KB
  52. coco8-pose/labels/val/000000000113.txt 1.46KB
  53. coco8-pose/labels/val/000000000136.txt 994B
  54. coco8-pose/labels/val/000000000151.txt 497B
  55. coco8-pose/LICENSE 34.3KB
  56. coco8-pose/README.md 693B
  57. CONTRIBUTING.md 5.45KB
  58. docker/
  59. docker/Dockerfile 3.6KB
  60. docker/Dockerfile-arm64 1.96KB
  61. docker/Dockerfile-conda 1.76KB
  62. docker/Dockerfile-cpu 2.49KB
  63. docker/Dockerfile-jetson 2.25KB
  64. docker/Dockerfile-python 2.41KB
  65. docker/Dockerfile-runner 1.69KB
  66. docs/
  67. docs/ar/
  68. docs/ar/datasets/
  69. docs/ar/datasets/index.md 12.53KB
  70. docs/ar/index.md 10.89KB
  71. docs/ar/models/
  72. docs/ar/models/fast-sam.md 11.94KB
  73. docs/ar/models/index.md 7.17KB
  74. docs/ar/models/mobile-sam.md 7.44KB
  75. docs/ar/models/rtdetr.md 8.33KB
  76. docs/ar/models/sam.md 17.02KB
  77. docs/ar/models/yolo-nas.md 9.85KB
  78. docs/ar/models/yolov3.md 7.58KB
  79. docs/ar/models/yolov4.md 8.62KB
  80. docs/ar/models/yolov5.md 12.39KB
  81. docs/ar/models/yolov6.md 9.09KB
  82. docs/ar/models/yolov7.md 8.75KB
  83. docs/ar/models/yolov8.md 20.87KB
  84. docs/ar/modes/
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  87. docs/ar/modes/index.md 6.43KB
  88. docs/ar/modes/predict.md 15.32KB
  89. docs/ar/modes/track.md 20.56KB
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  91. docs/ar/modes/val.md 7.9KB
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  93. docs/ar/tasks/
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  95. docs/ar/tasks/detect.md 13.29KB
  96. docs/ar/tasks/index.md 4.11KB
  97. docs/ar/tasks/pose.md 13.89KB
  98. docs/ar/tasks/segment.md 14.19KB
  99. docs/build_docs.py 4.54KB
  100. docs/build_reference.py 4.96KB
  101. docs/de/
  102. docs/de/datasets/
  103. docs/de/datasets/index.md 9.7KB
  104. docs/de/index.md 9.66KB
  105. docs/de/models/
  106. docs/de/models/fast-sam.md 10.29KB
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  108. docs/de/models/mobile-sam.md 6.12KB
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  110. docs/de/models/sam.md 13.94KB
  111. docs/de/models/yolo-nas.md 8.29KB
  112. docs/de/models/yolov3.md 6.46KB
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  114. docs/de/models/yolov5.md 11.62KB
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  117. docs/de/models/yolov8.md 19.24KB
  118. docs/de/modes/
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  121. docs/de/modes/index.md 5.17KB
  122. docs/de/modes/predict.md 13.79KB
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  126. docs/de/quickstart.md 10.79KB
  127. docs/de/tasks/
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  135. docs/en/datasets/
  136. docs/en/datasets/classify/
  137. docs/en/datasets/classify/caltech101.md 4.19KB
  138. docs/en/datasets/classify/caltech256.md 3.97KB
  139. docs/en/datasets/classify/cifar10.md 3.85KB
  140. docs/en/datasets/classify/cifar100.md 3.94KB
  141. docs/en/datasets/classify/fashion-mnist.md 3.73KB
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  144. docs/en/datasets/classify/imagenette.md 5.26KB
  145. docs/en/datasets/classify/imagewoof.md 4.8KB
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  148. docs/en/datasets/detect/
  149. docs/en/datasets/detect/argoverse.md 5.59KB
  150. docs/en/datasets/detect/coco.md 5.42KB
  151. docs/en/datasets/detect/coco8.md 3.99KB
  152. docs/en/datasets/detect/globalwheat2020.md 5.58KB
  153. docs/en/datasets/detect/index.md 5.75KB
  154. docs/en/datasets/detect/objects365.md 4.93KB
  155. docs/en/datasets/detect/open-images-v7.md 5.96KB
  156. docs/en/datasets/detect/sku-110k.md 4.83KB
  157. docs/en/datasets/detect/visdrone.md 5.19KB
  158. docs/en/datasets/detect/voc.md 5.32KB
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  162. docs/en/datasets/obb/dota-v2.md 5.5KB
  163. docs/en/datasets/obb/index.md 3.34KB
  164. docs/en/datasets/pose/
  165. docs/en/datasets/pose/coco.md 5.24KB
  166. docs/en/datasets/pose/coco8-pose.md 3.99KB
  167. docs/en/datasets/pose/index.md 6.83KB
  168. docs/en/datasets/pose/tiger-pose.md 4.5KB
  169. docs/en/datasets/segment/
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  171. docs/en/datasets/segment/coco8-seg.md 4.04KB
  172. docs/en/datasets/segment/index.md 7.19KB
  173. docs/en/datasets/track/
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  175. docs/en/guides/
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  179. docs/en/guides/heatmaps.md 14.35KB
  180. docs/en/guides/hyperparameter-tuning.md 9.58KB
  181. docs/en/guides/index.md 6.16KB
  182. docs/en/guides/instance-segmentation-and-tracking.md 5.82KB
  183. docs/en/guides/isolating-segmentation-objects.md 14.75KB
  184. docs/en/guides/kfold-cross-validation.md 12.32KB
  185. docs/en/guides/model-deployment-options.md 22.79KB
  186. docs/en/guides/object-counting.md 9.86KB
  187. docs/en/guides/raspberry-pi.md 8.21KB
  188. docs/en/guides/region-counting.md 5.01KB
  189. docs/en/guides/sahi-tiled-inference.md 7.36KB
  190. docs/en/guides/security-alarm-system.md 5.98KB
  191. docs/en/guides/triton-inference-server.md 4.99KB
  192. docs/en/guides/vision-eye.md 5.51KB
  193. docs/en/guides/workouts-monitoring.md 7.91KB
  194. docs/en/guides/yolo-common-issues.md 16.84KB
  195. docs/en/guides/yolo-performance-metrics.md 10.93KB
  196. docs/en/guides/yolo-thread-safe-inference.md 5.18KB
  197. docs/en/help/
  198. docs/en/help/CI.md 11.35KB
  199. docs/en/help/CLA.md 5.76KB
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  202. docs/en/help/environmental-health-safety.md 3.15KB
  203. docs/en/help/FAQ.md 3KB
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  205. docs/en/help/minimum_reproducible_example.md 3.62KB
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  208. docs/en/hub/
  209. docs/en/hub/app/
  210. docs/en/hub/app/android.md 9.63KB
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  219. docs/en/hub/quickstart.md 2.58KB
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  221. docs/en/integrations/
  222. docs/en/integrations/clearml.md 10.22KB
  223. docs/en/integrations/comet.md 8.82KB
  224. docs/en/integrations/dvc.md 9.33KB
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  226. docs/en/integrations/mlflow.md 5.34KB
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  228. docs/en/integrations/ray-tune.md 10.85KB
  229. docs/en/integrations/roboflow.md 15.87KB
  230. docs/en/models/
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  232. docs/en/models/index.md 5.3KB
  233. docs/en/models/mobile-sam.md 5.67KB
  234. docs/en/models/rtdetr.md 6.04KB
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  236. docs/en/models/yolo-nas.md 7.54KB
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  250. docs/en/modes/val.md 5.76KB
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  253. docs/en/reference/cfg/
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  276. docs/en/reference/hub/__init__.md 1.04KB
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资源介绍:

这个项目是一个基于YOLOv8-Pose的姿态识别系统，专门用于识别和分析人体姿态。项目采用了最新的YOLOv8-Pose算法，结合了COCO数据集的8种常见姿态，能够快速准确地识别人体的各种姿态。这个可以作为一个简单的项目案例，后续可以直接换成自己的数据去进行训练。 功能特点： 高效识别：使用了先进的YOLOv8-Pose算法，确保了识别的准确性和效率。 支持多种姿态：能够识别COCO数据集中定义的8种主要姿态。 实时处理能力：项目设计支持实时姿态识别，适用于视频监控、动态分析等场景。 使用方法： 环境要求：详细说明所需的操作系统、依赖库和运行环境。 安装步骤：提供项目安装和配置的具体指导。 运行指南：说明如何启动姿态识别任务，包括命令行参数等。

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[Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks. We hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8 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://ultralytics.com/license). YOLOv8 performance plots
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##
Documentation
See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for full documentation on training, validation, prediction and deployment.
Install Pip install the ultralytics package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). [![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 ``` For alternative installation methods including [Conda](https://anaconda.org/conda-forge/ultralytics), [Docker](https://hub.docker.com/r/ultralytics/ultralytics), and Git, please refer to the [Quickstart Guide](https://docs.ultralytics.com/quickstart).
Usage #### CLI YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command: ```bash yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' ``` `yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8 [CLI Docs](https://docs.ultralytics.com/usage/cli) for examples. #### Python YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above: ```python from ultralytics import YOLO # Load a model model = YOLO("yolov8n.yaml") # build a new model from scratch model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training) # Use the model model.train(data="coco128.yaml", epochs=3) # train the model metrics = model.val() # evaluate model performance on the validation set results = model("https://ultralytics.com/images/bus.jpg") # predict on an image path = model.export(format="onnx") # export the model to ONNX format ``` See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
##
Models
YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/c
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