{"id":13641022,"url":"https://github.com/WangQvQ/YOLOMagic","last_synced_at":"2025-04-20T07:32:01.132Z","repository":{"id":37399200,"uuid":"493106609","full_name":"WangQvQ/YOLOMagic","owner":"WangQvQ","description":"YOLO Magic🪄 is an extension based on Ultralytics' YOLOv5, designed to provide more powerful functionality and simpler operations for visual tasks.","archived":false,"fork":false,"pushed_at":"2024-04-25T05:01:14.000Z","size":14647,"stargazers_count":509,"open_issues_count":0,"forks_count":74,"subscribers_count":3,"default_branch":"main","last_synced_at":"2024-09-20T03:06:54.624Z","etag":null,"topics":["deep-learning","gradio","machine-learning","onnx","pytorch","tflite","yolo","yolov5"],"latest_commit_sha":null,"homepage":"","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/WangQvQ.png","metadata":{"files":{"readme":"README-ch.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-05-17T05:24:39.000Z","updated_at":"2024-08-19T16:17:01.000Z","dependencies_parsed_at":"2024-01-14T12:08:46.933Z","dependency_job_id":"fba1ea1f-15ae-4f48-aee5-2e6534297140","html_url":"https://github.com/WangQvQ/YOLOMagic","commit_stats":null,"previous_names":["wangqvq/yolov5_magic"],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WangQvQ%2FYOLOMagic","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WangQvQ%2FYOLOMagic/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WangQvQ%2FYOLOMagic/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WangQvQ%2FYOLOMagic/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/WangQvQ","download_url":"https://codeload.github.com/WangQvQ/YOLOMagic/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223779728,"owners_count":17201287,"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","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":["deep-learning","gradio","machine-learning","onnx","pytorch","tflite","yolo","yolov5"],"created_at":"2024-08-02T01:01:16.961Z","updated_at":"2024-11-09T11:30:40.866Z","avatar_url":"https://github.com/WangQvQ.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# YOLO Magic🚀 - 强化YOLOv5的视觉任务框架\n\u003cdiv align=\"center\"\u003e\n\n![image](https://github.com/WangQvQ/Yolov5_Magic/assets/58406737/24a7718c-2403-46b7-a81e-205ebeb9869e)\n[English](README.en-EN.md)|[简体中文](README.md)\u003cbr\u003e\n \u003c/div\u003e\n \nYOLO Magic🚀是一个基于[Ultralytics](https://ultralytics.com) YOLOv5的扩展，旨在为视觉任务提供更强大的功能和更简单的操作。它在YOLOv5的基础上引入了丰富的网络模块，并提供了直观易用的Web操作界面，旨在为新手和专业用户提供更大的便利和灵活性。\n\n## 主要特性\n\n### 1. 强大的网络模块扩展\n\nYOLO Magic🚀引入了一系列强大的网络模块，旨在扩展YOLOv5的功能，并为用户提供更多的选择和可能性：\n\n- **空间金字塔模块**：包括SPP、SPPF、ASPP、SPPCSPC、SPPFCSPC等，这些模块旨在在不同的空间尺度上捕获目标，并增强模型的视觉感知能力。\n\n- **特征融合结构**：我们提供了多样化的特征融合结构，如FPN、PAN、BIFPN等，这些结构旨在有效地融合来自不同层级的特征信息，从而提高模型的目标检测和定位性能。\n\n- **新型骨干网络**：YOLO Magic🚀支持多种预训练的骨干网络，包括EfficientNet、ShuffleNet等，这些骨干网络提供了额外的选择，以提高模型的性能和效率。\n\n- **丰富的注意力机制**：我们提供多种注意力机制，这些机制可以轻松嵌入到您的模型中，以增强对目标的关注度，并提升模型的检测性能。\n\n### 2. 简单易用的Web操作页面\n\nYOLO Magic🚀通过直观的Web操作页面，大大简化了模型推理过程，无需繁琐的命令行操作，您可以轻松完成以下任务：\n\n- **图片推理**：只需进行简单的拖放和配置，即可执行图片推理和目标检测。您可以自由调整置信度、阈值，上传图像并截取感兴趣的区域。\n- **视频推理**：TODO\n\n![image](https://github.com/WangQvQ/Yolov5_Magic/assets/58406737/97a2432a-386b-4d7c-b941-f745b4b38db3)\n\n\n## 为什么选择YOLO Magic🚀\n\n- **更强大的性能**：引入了先进的网络模块，提升了模型的性能和准确性。\n\n- **更简单的操作**：Web界面使操作更加直观和友好，即使是初学者也能快速上手。\n\n- **可定制性**：支持各种自定义配置，满足不同场景和任务的需求。\n\n- **社区支持**：YOLO Magic🚀拥有一个活跃的社区，提供丰富的教程和资源，帮助用户充分利用这一强大的工具。\n\n## 快速开始\n\n你可以通过以下步骤快速开始使用YOLO Magic🚀：\n\n**安装**\n\n```bash\ngit clone https://github.com/ultralytics/yolov5  # 克隆仓库\ncd yolov5\npip install -r requirements.txt  # 安装环境\n```\n\n**detect.py 推理**\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  # 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\n```\n\n**Web 页面推理**\n\n使用 `Gradio` 搭建的页面启动一个 `Web` 页面快速启动\n\n```bash\npython detect_web.py\n```\n\n**训练**\n\n以下指令再现了 YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) 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 --epochs 300 --weights '' --cfg yolov5n.yaml  --batch-size 128\n                                                                 yolov5s                    64\n                                                                 yolov5m                    40\n                                                                 yolov5l                    24\n                                                                 yolov5x                    16\n```\n\n![img](https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png)\n\n**验证**\n\n使用 `val.py` 对你的模型实现验证。\n\n```bash\npython val.py --weights yolov5s.pt --task test\n\t\t\t\t\t  val\n```\n\n## 贡献\n\n我们欢迎开发者和研究者一起贡献代码，共同改进YOLO Magic🚀。\n\n如果你有任何问题或建议，欢迎你提出issue。我们的社区成员将很高兴地为你提供帮助和支持。\n\n## 许可证\n\n本项目的代码和文档现在采用 GNU Affero General Public License 3.0（AGPL-3.0）许可证。详细的许可证内容请参阅附带的 [LICENSE](LICENSE) 文件。\n\n这意味着，任何使用、修改和重新分发本项目的用户必须在提供该项目的网络服务时，公开源代码。请详细阅读许可证以了解更多信息。\n\n---\n\n无论你是一个新手还是一个经验丰富的视觉任务研究者，YOLO Magic🚀都将为你提供一个强大、易用的工具，助力你在计算机视觉领域取得成功。\n\n*探索视觉任务的新境界，尽在YOLO Magic🚀。* 🌟👁️\n\n\n-----\n\u003cdiv  align=\"center\"\u003e\n\t\n\u003cimg src=\"https://raw.githubusercontent.com/Tarikul-Islam-Anik/Animated-Fluent-Emojis/master/Emojis/Smilies/Face%20with%20Spiral%20Eyes.png\" width=\"10%\" alt=\"Broken system!\"/\u003e\n\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\n\u003cimg src=\"https://raw.githubusercontent.com/Tarikul-Islam-Anik/Animated-Fluent-Emojis/master/Emojis/Smilies/Relieved%20Face.png\" width=\"10%\" alt=\"It's working!\"/\u003e\n\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\n\u003cimg src=\"https://raw.githubusercontent.com/Tarikul-Islam-Anik/Animated-Fluent-Emojis/master/Emojis/Smilies/Astonished%20Face.png\" width=\"10%\" alt=\"It's working but you don't know how!\"/\u003e\u003cbr\u003e\n\n\n\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FWangQvQ%2FYOLOMagic","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FWangQvQ%2FYOLOMagic","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FWangQvQ%2FYOLOMagic/lists"}