{"id":18811388,"url":"https://github.com/enot-autodl/yoqo","last_synced_at":"2025-09-05T00:34:01.610Z","repository":{"id":163867251,"uuid":"639310062","full_name":"ENOT-AutoDL/yoqo","owner":"ENOT-AutoDL","description":null,"archived":false,"fork":false,"pushed_at":"2023-12-18T11:10:47.000Z","size":2533,"stargazers_count":0,"open_issues_count":7,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-05-22T03:37:35.645Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ENOT-AutoDL.png","metadata":{"files":{"readme":".github/README_cn.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":".github/CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":".github/SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-05-11T07:55:59.000Z","updated_at":"2023-05-11T09:29:48.000Z","dependencies_parsed_at":"2024-11-07T23:28:49.709Z","dependency_job_id":"f1e35ece-b974-45fc-8418-423cd5b83728","html_url":"https://github.com/ENOT-AutoDL/yoqo","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ENOT-AutoDL/yoqo","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ENOT-AutoDL%2Fyoqo","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ENOT-AutoDL%2Fyoqo/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ENOT-AutoDL%2Fyoqo/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ENOT-AutoDL%2Fyoqo/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ENOT-AutoDL","download_url":"https://codeload.github.com/ENOT-AutoDL/yoqo/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ENOT-AutoDL%2Fyoqo/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273694955,"owners_count":25151480,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-04T02:00:08.968Z","response_time":61,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-07T23:26:06.330Z","updated_at":"2025-09-05T00:33:58.109Z","avatar_url":"https://github.com/ENOT-AutoDL.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\n\u003cp\u003e\n   \u003ca align=\"left\" href=\"https://ultralytics.com/yolov5\" target=\"_blank\"\u003e\n   \u003cimg width=\"850\" src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cbr\u003e\n\n[English](../README.md) | 简体中文\n\u003cdiv\u003e\n   \u003ca href=\"https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml\"\u003e\u003cimg src=\"https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg\" alt=\"CI CPU testing\"\u003e\u003c/a\u003e\n   \u003ca href=\"https://zenodo.org/badge/latestdoi/264818686\"\u003e\u003cimg src=\"https://zenodo.org/badge/264818686.svg\" alt=\"YOLOv5 Citation\"\u003e\u003c/a\u003e\n   \u003ca href=\"https://hub.docker.com/r/ultralytics/yolov5\"\u003e\u003cimg src=\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\" alt=\"Docker Pulls\"\u003e\u003c/a\u003e\n   \u003cbr\u003e\n   \u003ca href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"\u003e\u003c/a\u003e\n   \u003ca href=\"https://www.kaggle.com/ultralytics/yolov5\"\u003e\u003cimg src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"\u003e\u003c/a\u003e\n   \u003ca href=\"https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg\"\u003e\u003cimg src=\"https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack\" alt=\"Join Forum\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n\u003cbr\u003e\n\u003cp\u003e\nYOLOv5🚀是一个在COCO数据集上预训练的物体检测架构和模型系列，它代表了\u003ca href=\"https://ultralytics.com\"\u003eUltralytics\u003c/a\u003e对未来视觉AI方法的公开研究，其中包含了在数千小时的研究和开发中所获得的经验和最佳实践。\n\u003c/p\u003e\n\n\u003cdiv align=\"center\"\u003e\n   \u003ca href=\"https://github.com/ultralytics\"\u003e\n   \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png\" width=\"2%\"/\u003e\n   \u003c/a\u003e\n   \u003cimg width=\"2%\" /\u003e\n   \u003ca href=\"https://www.linkedin.com/company/ultralytics\"\u003e\n   \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png\" width=\"2%\"/\u003e\n   \u003c/a\u003e\n   \u003cimg width=\"2%\" /\u003e\n   \u003ca href=\"https://twitter.com/ultralytics\"\u003e\n   \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png\" width=\"2%\"/\u003e\n   \u003c/a\u003e\n   \u003cimg width=\"2%\" /\u003e\n   \u003ca href=\"https://www.producthunt.com/@glenn_jocher\"\u003e\n   \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png\" width=\"2%\"/\u003e\n   \u003c/a\u003e\n   \u003cimg width=\"2%\" /\u003e\n   \u003ca href=\"https://youtube.com/ultralytics\"\u003e\n   \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png\" width=\"2%\"/\u003e\n   \u003c/a\u003e\n   \u003cimg width=\"2%\" /\u003e\n   \u003ca href=\"https://www.facebook.com/ultralytics\"\u003e\n   \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png\" width=\"2%\"/\u003e\n   \u003c/a\u003e\n   \u003cimg width=\"2%\" /\u003e\n   \u003ca href=\"https://www.instagram.com/ultralytics/\"\u003e\n   \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png\" width=\"2%\"/\u003e\n   \u003c/a\u003e\n\u003c/div\u003e\n\n\u003c!--\n\u003ca align=\"center\" href=\"https://ultralytics.com/yolov5\" target=\"_blank\"\u003e\n\u003cimg width=\"800\" src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png\"\u003e\u003c/a\u003e\n--\u003e\n\n\u003c/div\u003e\n\n## \u003cdiv align=\"center\"\u003e文件\u003c/div\u003e\n\n请参阅[YOLOv5 Docs](https://docs.ultralytics.com)，了解有关训练、测试和部署的完整文件。\n\n## \u003cdiv align=\"center\"\u003e快速开始案例\u003c/div\u003e\n\n\u003cdetails open\u003e\n\u003csummary\u003e安装\u003c/summary\u003e\n\n在[**Python\u003e=3.7.0**](https://www.python.org/) 的环境中克隆版本仓并安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt)，包括[**PyTorch\u003e=1.7**](https://pytorch.org/get-started/locally/)。\n```bash\ngit clone https://github.com/ultralytics/yolov5  # 克隆\ncd yolov5\npip install -r requirements.txt  # 安装\n```\n\n\u003c/details\u003e\n\n\u003cdetails open\u003e\n\u003csummary\u003e推理\u003c/summary\u003e\n\nYOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 推理. [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从最新YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases)下载。\n\n```python\nimport torch\n\n# 模型\nmodel = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5n - yolov5x6, custom\n\n# 图像\nimg = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list\n\n# 推理\nresults = model(img)\n\n# 结果\nresults.print()  # or .show(), .save(), .crop(), .pandas(), etc.\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e用 detect.py 进行推理\u003c/summary\u003e\n\n`detect.py` 在各种数据源上运行推理, 其会从最新的 YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases) 中自动下载 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 并将检测结果保存到 `runs/detect` 目录。\n\n```bash\npython detect.py --source 0  # 网络摄像头\n                          img.jpg  # 图像\n                          vid.mp4  # 视频\n                          path/  # 文件夹\n                          path/*.jpg  # glob\n                          'https://youtu.be/Zgi9g1ksQHc'  # YouTube\n                          'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP 流\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e训练\u003c/summary\u003e\n\n以下指令再现了 YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)\n数据集结果. [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) 自动从最新的YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases) 中下载。YOLOv5n/s/m/l/x的训练时间在V100 GPU上是 1/2/4/6/8天（多GPU倍速）. 尽可能使用最大的 `--batch-size`, 或通过 `--batch-size -1` 来实现 YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092). 批量大小显示为 V100-16GB。\n\n```bash\npython train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128\n                                       yolov5s                                64\n                                       yolov5m                                40\n                                       yolov5l                                24\n                                       yolov5x                                16\n```\n\n\u003cimg width=\"800\" src=\"https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png\"\u003e\n\n\u003c/details\u003e\n\n\u003cdetails open\u003e\n\u003csummary\u003e教程\u003c/summary\u003e\n\n- [训练自定义数据](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)  🚀 推荐\n- [获得最佳训练效果的技巧](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)  ☘️ 推荐\n- [使用 Weights \u0026 Biases 记录实验](https://github.com/ultralytics/yolov5/issues/1289)  🌟 新\n- [Roboflow：数据集、标签和主动学习](https://github.com/ultralytics/yolov5/issues/4975)  🌟 新\n- [多GPU训练](https://github.com/ultralytics/yolov5/issues/475)\n- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)  ⭐ 新\n- [TFLite, ONNX, CoreML, TensorRT 导出](https://github.com/ultralytics/yolov5/issues/251) 🚀\n- [测试时数据增强 (TTA)](https://github.com/ultralytics/yolov5/issues/303)\n- [模型集成](https://github.com/ultralytics/yolov5/issues/318)\n- [模型剪枝/稀疏性](https://github.com/ultralytics/yolov5/issues/304)\n- [超参数进化](https://github.com/ultralytics/yolov5/issues/607)\n- [带有冻结层的迁移学习](https://github.com/ultralytics/yolov5/issues/1314) ⭐ 新\n- [架构概要](https://github.com/ultralytics/yolov5/issues/6998) ⭐ 新\n\n\u003c/details\u003e\n\n## \u003cdiv align=\"center\"\u003e环境\u003c/div\u003e\n\n使用经过我们验证的环境，几秒钟就可以开始。点击下面的每个图标了解详情。\n\n\u003cdiv align=\"center\"\u003e\n    \u003ca href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"\u003e\n        \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png\" width=\"15%\"/\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://www.kaggle.com/ultralytics/yolov5\"\u003e\n        \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png\" width=\"15%\"/\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://hub.docker.com/r/ultralytics/yolov5\"\u003e\n        \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png\" width=\"15%\"/\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart\"\u003e\n        \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png\" width=\"15%\"/\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart\"\u003e\n        \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png\" width=\"15%\"/\u003e\n    \u003c/a\u003e\n\u003c/div\u003e\n\n## \u003cdiv align=\"center\"\u003e如何与第三方集成\u003c/div\u003e\n\n\u003cdiv align=\"center\"\u003e\n    \u003ca href=\"https://wandb.ai/site?utm_campaign=repo_yolo_readme\"\u003e\n        \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb-long.png\" width=\"49%\"/\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://roboflow.com/?ref=ultralytics\"\u003e\n        \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow-long.png\" width=\"49%\"/\u003e\n    \u003c/a\u003e\n\u003c/div\u003e\n\n|Weights and Biases|Roboflow ⭐ 新|\n|:-:|:-:|\n|通过 [Weights \u0026 Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme) 自动跟踪和可视化你在云端的所有YOLOv5训练运行状态。|标记并将您的自定义数据集直接导出到YOLOv5，以便用 [Roboflow](https://roboflow.com/?ref=ultralytics) 进行训练。 |\n\n\u003c!-- ## \u003cdiv align=\"center\"\u003eCompete and Win\u003c/div\u003e\n\nWe are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10,000** in cash prizes!\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/ultralytics/yolov5/discussions/3213\"\u003e\n  \u003cimg width=\"850\" src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-export-competition.png\"\u003e\u003c/a\u003e\n\u003c/p\u003e --\u003e\n\n## \u003cdiv align=\"center\"\u003e为什么选择 YOLOv5\u003c/div\u003e\n\n\u003cp align=\"left\"\u003e\u003cimg width=\"800\" src=\"https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png\"\u003e\u003c/p\u003e\n\u003cdetails\u003e\n  \u003csummary\u003eYOLOv5-P5 640 图像 (点击扩展)\u003c/summary\u003e\n\n\u003cp align=\"left\"\u003e\u003cimg width=\"800\" src=\"https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png\"\u003e\u003c/p\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n  \u003csummary\u003e图片注释 (点击扩展)\u003c/summary\u003e\n\n- **COCO AP val** 表示 mAP@0.5:0.95 在5000张图像的[COCO val2017](http://cocodataset.org)数据集上，在256到1536的不同推理大小上测量的指标。\n- **GPU Speed** 衡量的是在 [COCO val2017](http://cocodataset.org) 数据集上使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100实例在批量大小为32时每张图像的平均推理时间。\n- **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) ，批量大小设置为 8。\n- 复现 mAP 方法: `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`\n\n\u003c/details\u003e\n\n### 预训练检查点\n\n|Model |size\u003cbr\u003e\u003csup\u003e(pixels) |mAP\u003csup\u003eval\u003cbr\u003e0.5:0.95 |mAP\u003csup\u003eval\u003cbr\u003e0.5 |Speed\u003cbr\u003e\u003csup\u003eCPU b1\u003cbr\u003e(ms) |Speed\u003cbr\u003e\u003csup\u003eV100 b1\u003cbr\u003e(ms) |Speed\u003cbr\u003e\u003csup\u003eV100 b32\u003cbr\u003e(ms) |params\u003cbr\u003e\u003csup\u003e(M) |FLOPs\u003cbr\u003e\u003csup\u003e@640 (B)\n|---                    |---  |---    |---    |---    |---    |---    |---    |---\n|[YOLOv5n][assets]      |640  |28.0   |45.7   |**45** |**6.3**|**0.6**|**1.9**|**4.5**\n|[YOLOv5s][assets]      |640  |37.4   |56.8   |98     |6.4    |0.9    |7.2    |16.5\n|[YOLOv5m][assets]      |640  |45.4   |64.1   |224    |8.2    |1.7    |21.2   |49.0\n|[YOLOv5l][assets]      |640  |49.0   |67.3   |430    |10.1   |2.7    |46.5   |109.1\n|[YOLOv5x][assets]      |640  |50.7   |68.9   |766    |12.1   |4.8    |86.7   |205.7\n|                       |     |       |       |       |       |       |       |\n|[YOLOv5n6][assets]     |1280 |36.0   |54.4   |153    |8.1    |2.1    |3.2    |4.6\n|[YOLOv5s6][assets]     |1280 |44.8   |63.7   |385    |8.2    |3.6    |12.6   |16.8\n|[YOLOv5m6][assets]     |1280 |51.3   |69.3   |887    |11.1   |6.8    |35.7   |50.0\n|[YOLOv5l6][assets]     |1280 |53.7   |71.3   |1784   |15.8   |10.5   |76.8   |111.4\n|[YOLOv5x6][assets]\u003cbr\u003e+ [TTA][TTA]|1280\u003cbr\u003e1536 |55.0\u003cbr\u003e**55.8** |72.7\u003cbr\u003e**72.7** |3136\u003cbr\u003e- |26.2\u003cbr\u003e- |19.4\u003cbr\u003e- |140.7\u003cbr\u003e- |209.8\u003cbr\u003e-\n\n\u003cdetails\u003e\n  \u003csummary\u003e表格注释 (点击扩展)\u003c/summary\u003e\n\n- 所有检查点都以默认设置训练到300个时期. Nano和Small模型用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, 其他模型使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).\n- **mAP\u003csup\u003eval\u003c/sup\u003e** 值是 [COCO val2017](http://cocodataset.org) 数据集上的单模型单尺度的值。\n\u003cbr\u003e复现方法: `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`\n- 使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) 实例对COCO val图像的平均速度。不包括NMS时间（~1 ms/img)\n\u003cbr\u003e复现方法: `python val.py --data coco.yaml --img 640 --task speed --batch 1`\n- **TTA** [测试时数据增强](https://github.com/ultralytics/yolov5/issues/303) 包括反射和比例增强.\n\u003cbr\u003e复现方法: `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`\n\n\u003c/details\u003e\n\n## \u003cdiv align=\"center\"\u003e贡献\u003c/div\u003e\n\n我们重视您的意见! 我们希望给大家提供尽可能的简单和透明的方式对 YOLOv5 做出贡献。开始之前请先点击并查看我们的 [贡献指南](CONTRIBUTING.md)，填写[YOLOv5调查问卷](https://ultralytics.com/survey?utm_source=github\u0026utm_medium=social\u0026utm_campaign=Survey) 来向我们发送您的经验反馈。真诚感谢我们所有的贡献者!\n\n\u003c!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 --\u003e\n\u003ca href=\"https://github.com/ultralytics/yolov5/graphs/contributors\"\u003e\u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/image-contributors-1280.png\" /\u003e\u003c/a\u003e\n\n## \u003cdiv align=\"center\"\u003e联系\u003c/div\u003e\n\n关于YOLOv5的漏洞和功能问题，请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues)。商业咨询或技术支持服务请访问[https://ultralytics.com/contact](https://ultralytics.com/contact)。\n\n\u003cbr\u003e\n\n\u003cdiv align=\"center\"\u003e\n    \u003ca href=\"https://github.com/ultralytics\"\u003e\n        \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png\" width=\"3%\"/\u003e\n    \u003c/a\u003e\n    \u003cimg width=\"3%\" /\u003e\n    \u003ca href=\"https://www.linkedin.com/company/ultralytics\"\u003e\n        \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png\" width=\"3%\"/\u003e\n    \u003c/a\u003e\n    \u003cimg width=\"3%\" /\u003e\n    \u003ca href=\"https://twitter.com/ultralytics\"\u003e\n        \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png\" width=\"3%\"/\u003e\n    \u003c/a\u003e\n    \u003cimg width=\"3%\" /\u003e\n    \u003ca href=\"https://www.producthunt.com/@glenn_jocher\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png\" width=\"3%\"/\u003e\n    \u003c/a\u003e\n    \u003cimg width=\"3%\" /\u003e\n    \u003ca href=\"https://youtube.com/ultralytics\"\u003e\n        \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png\" width=\"3%\"/\u003e\n    \u003c/a\u003e\n    \u003cimg width=\"3%\" /\u003e\n    \u003ca href=\"https://www.facebook.com/ultralytics\"\u003e\n        \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png\" width=\"3%\"/\u003e\n    \u003c/a\u003e\n    \u003cimg width=\"3%\" /\u003e\n    \u003ca href=\"https://www.instagram.com/ultralytics/\"\u003e\n        \u003cimg src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png\" width=\"3%\"/\u003e\n    \u003c/a\u003e\n\u003c/div\u003e\n\n[assets]: https://github.com/ultralytics/yolov5/releases\n[tta]: https://github.com/ultralytics/yolov5/issues/303\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fenot-autodl%2Fyoqo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fenot-autodl%2Fyoqo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fenot-autodl%2Fyoqo/lists"}