[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-572":3},{"id":4,"name":5,"fullName":6,"owner":5,"repo":5,"description":7,"homepage":8,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":16,"rankGlobal":9,"rankLanguage":9,"license":20,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":23,"hasPages":21,"topics":24,"createdAt":9,"pushedAt":9,"updatedAt":44,"readmeContent":45,"aiSummary":46,"trendingCount":15,"starSnapshotCount":15,"syncStatus":47,"lastSyncTime":48,"discoverSource":49},572,"ultralytics","ultralytics\u002Fultralytics","Ultralytics YOLO 🚀","https:\u002F\u002Fdocs.ultralytics.com",null,"Python",58292,11176,257,74,0,45,301,1274,215,"GNU Affero General Public License v3.0",false,"main",true,[25,26,27,28,29,30,31,32,33,34,35,36,37,38,5,39,40,41,42,43],"cli","computer-vision","deep-learning","hub","image-classification","instance-segmentation","machine-learning","object-detection","pose-estimation","python","pytorch","rotated-object-detection","segment-anything","tracking","yolo","yolo-world","yolo11","yolo26","yolov8","2026-06-12 02:00:15","\u003Cdiv align=\"center\">\n  \u003Cp>\n    \u003Ca href=\"https:\u002F\u002Fplatform.ultralytics.com\u002F?utm_source=github&utm_medium=referral&utm_campaign=platform_launch&utm_content=banner&utm_term=ultralytics_github\" target=\"_blank\">\n      \u003Cimg width=\"100%\" src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fultralytics\u002Fassets\u002Fmain\u002Fyolov8\u002Fbanner-yolov8.png\" alt=\"Ultralytics YOLO banner\">\u003C\u002Fa>\n  \u003C\u002Fp>\n\n[中文](https:\u002F\u002Fdocs.ultralytics.com\u002Fzh\u002F) | [한국어](https:\u002F\u002Fdocs.ultralytics.com\u002Fko\u002F) | 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href=\"https:\u002F\u002Fclickpy.clickhouse.com\u002Fdashboard\u002Fultralytics\">\u003Cimg src=\"https:\u002F\u002Fstatic.pepy.tech\u002Fbadge\u002Fultralytics\" alt=\"Ultralytics Downloads\">\u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fdiscord.com\u002Finvite\u002Fultralytics\">\u003Cimg alt=\"Ultralytics Discord\" src=\"https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\">\u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fcommunity.ultralytics.com\u002F\">\u003Cimg alt=\"Ultralytics Forums\" src=\"https:\u002F\u002Fimg.shields.io\u002Fdiscourse\u002Fusers?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue\">\u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fwww.reddit.com\u002Fr\u002Fultralytics\u002F\">\u003Cimg alt=\"Ultralytics Reddit\" src=\"https:\u002F\u002Fimg.shields.io\u002Freddit\u002Fsubreddit-subscribers\u002Fultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue\">\u003C\u002Fa>\n    \u003Cbr>\n    \u003Ca href=\"https:\u002F\u002Fconsole.paperspace.com\u002Fgithub\u002Fultralytics\u002Fultralytics\">\u003Cimg src=\"https:\u002F\u002Fassets.paperspace.io\u002Fimg\u002Fgradient-badge.svg\" alt=\"Run Ultralytics on Gradient\">\u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fultralytics\u002Fultralytics\u002Fblob\u002Fmain\u002Fexamples\u002Ftutorial.ipynb\">\u003Cimg src=\"https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\" alt=\"Open Ultralytics In Colab\">\u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fwww.kaggle.com\u002Fmodels\u002Fultralytics\u002Fyolo26\">\u003Cimg src=\"https:\u002F\u002Fkaggle.com\u002Fstatic\u002Fimages\u002Fopen-in-kaggle.svg\" alt=\"Open Ultralytics In Kaggle\">\u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fmybinder.org\u002Fv2\u002Fgh\u002Fultralytics\u002Fultralytics\u002FHEAD?labpath=examples%2Ftutorial.ipynb\">\u003Cimg src=\"https:\u002F\u002Fmybinder.org\u002Fbadge_logo.svg\" alt=\"Open Ultralytics In Binder\">\u003C\u002Fa>\n\u003C\u002Fdiv>\n\u003C\u002Fdiv>\n\u003Cbr>\n\n[Ultralytics](https:\u002F\u002Fwww.ultralytics.com\u002F) creates cutting-edge, state-of-the-art (SOTA) [YOLO models](https:\u002F\u002Fwww.ultralytics.com\u002Fyolo) built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are **fast**, **accurate**, and **easy to use**. They excel at [object detection](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fdetect\u002F), [tracking](https:\u002F\u002Fdocs.ultralytics.com\u002Fmodes\u002Ftrack\u002F), [instance segmentation](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fsegment\u002F), [image classification](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fclassify\u002F), and [pose estimation](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fpose\u002F) tasks.\n\nFind detailed documentation in the [Ultralytics Docs](https:\u002F\u002Fdocs.ultralytics.com\u002F). Get support via [GitHub Issues](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fultralytics\u002Fissues\u002Fnew\u002Fchoose). Join discussions on [Discord](https:\u002F\u002Fdiscord.com\u002Finvite\u002Fultralytics), [Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Fultralytics\u002F), and the [Ultralytics Community Forums](https:\u002F\u002Fcommunity.ultralytics.com\u002F)!\n\nRequest an Enterprise License for commercial use at [Ultralytics Licensing](https:\u002F\u002Fwww.ultralytics.com\u002Flicense).\n\n\u003Ca href=\"https:\u002F\u002Fplatform.ultralytics.com\u002Fultralytics\u002Fyolo26\" target=\"_blank\">\n  \u003Cimg width=\"100%\" src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fultralytics\u002Fassets\u002Frefs\u002Fheads\u002Fmain\u002Fyolo\u002Fperformance-comparison.png\" alt=\"YOLO26 performance plots\">\n\u003C\u002Fa>\n\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fultralytics\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-github.png\" width=\"2%\" alt=\"Ultralytics GitHub\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"2%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fultralytics\u002F\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-linkedin.png\" width=\"2%\" alt=\"Ultralytics LinkedIn\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"2%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Ftwitter.com\u002Fultralytics\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-twitter.png\" width=\"2%\" alt=\"Ultralytics Twitter\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"2%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fultralytics?sub_confirmation=1\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-youtube.png\" width=\"2%\" alt=\"Ultralytics YouTube\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"2%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fwww.tiktok.com\u002F@ultralytics\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-tiktok.png\" width=\"2%\" alt=\"Ultralytics TikTok\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"2%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fultralytics.com\u002Fbilibili\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-bilibili.png\" width=\"2%\" alt=\"Ultralytics BiliBili\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"2%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fdiscord.com\u002Finvite\u002Fultralytics\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-discord.png\" width=\"2%\" alt=\"Ultralytics Discord\">\u003C\u002Fa>\n\u003C\u002Fdiv>\n\n## 📄 Documentation\n\nSee below for quickstart installation and usage examples. For comprehensive guidance on training, validation, prediction, and deployment, refer to our full [Ultralytics Docs](https:\u002F\u002Fdocs.ultralytics.com\u002F).\n\n\u003Cdetails open>\n\u003Csummary>Install\u003C\u002Fsummary>\n\nInstall the `ultralytics` package, including all [requirements](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fultralytics\u002Fblob\u002Fmain\u002Fpyproject.toml), in a [**Python>=3.8**](https:\u002F\u002Fwww.python.org\u002F) environment with [**PyTorch>=1.8**](https:\u002F\u002Fpytorch.org\u002Fget-started\u002Flocally\u002F).\n\n[![PyPI - Version](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fultralytics?logo=pypi&logoColor=white)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fultralytics\u002F) [![Ultralytics Downloads](https:\u002F\u002Fstatic.pepy.tech\u002Fbadge\u002Fultralytics)](https:\u002F\u002Fclickpy.clickhouse.com\u002Fdashboard\u002Fultralytics) [![PyPI - Python Version](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002Fultralytics?logo=python&logoColor=gold)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fultralytics\u002F)\n\n```bash\npip install ultralytics\n```\n\nFor alternative installation methods, including [Conda](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fultralytics), [Docker](https:\u002F\u002Fhub.docker.com\u002Fr\u002Fultralytics\u002Fultralytics), and building from source via Git, please consult the [Quickstart Guide](https:\u002F\u002Fdocs.ultralytics.com\u002Fquickstart\u002F).\n\n[![Conda Version](https:\u002F\u002Fimg.shields.io\u002Fconda\u002Fvn\u002Fconda-forge\u002Fultralytics?logo=condaforge)](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fultralytics) [![Docker Image Version](https:\u002F\u002Fimg.shields.io\u002Fdocker\u002Fv\u002Fultralytics\u002Fultralytics?sort=semver&logo=docker)](https:\u002F\u002Fhub.docker.com\u002Fr\u002Fultralytics\u002Fultralytics) [![Ultralytics Docker Pulls](https:\u002F\u002Fimg.shields.io\u002Fdocker\u002Fpulls\u002Fultralytics\u002Fultralytics?logo=docker)](https:\u002F\u002Fhub.docker.com\u002Fr\u002Fultralytics\u002Fultralytics)\n\n\u003C\u002Fdetails>\n\n\u003Cdetails open>\n\u003Csummary>Usage\u003C\u002Fsummary>\n\n### CLI\n\nYou can use Ultralytics YOLO directly from the Command Line Interface (CLI) with the `yolo` command:\n\n```bash\n# Predict using a pretrained YOLO model (e.g., YOLO26n) on an image\nyolo predict model=yolo26n.pt source='https:\u002F\u002Fultralytics.com\u002Fimages\u002Fbus.jpg'\n```\n\nThe `yolo` command supports various tasks and modes, accepting additional arguments like `imgsz=640`. Explore the YOLO [CLI Docs](https:\u002F\u002Fdocs.ultralytics.com\u002Fusage\u002Fcli\u002F) for more examples.\n\n### Python\n\nUltralytics YOLO can also be integrated directly into your Python projects. It accepts the same [configuration arguments](https:\u002F\u002Fdocs.ultralytics.com\u002Fusage\u002Fcfg\u002F) as the CLI:\n\n```python\nfrom ultralytics import YOLO\n\n# Load a pretrained YOLO26n model\nmodel = YOLO(\"yolo26n.pt\")\n\n# Train the model on the COCO8 dataset for 100 epochs\ntrain_results = model.train(\n    data=\"coco8.yaml\",  # Path to dataset configuration file\n    epochs=100,  # Number of training epochs\n    imgsz=640,  # Image size for training\n    device=\"cpu\",  # Device to run on (e.g., 'cpu', 0, [0,1,2,3])\n)\n\n# Evaluate the model's performance on the validation set\nmetrics = model.val()\n\n# Perform object detection on an image\nresults = model(\"path\u002Fto\u002Fimage.jpg\")  # Predict on an image\nresults[0].show()  # Display results\n\n# Export the model to ONNX format for deployment\npath = model.export(format=\"onnx\")  # Returns the path to the exported model\n```\n\nDiscover more examples in the YOLO [Python Docs](https:\u002F\u002Fdocs.ultralytics.com\u002Fusage\u002Fpython\u002F).\n\n\u003C\u002Fdetails>\n\n## ✨ Models\n\nUltralytics supports a wide range of YOLO models, from early versions like [YOLOv3](https:\u002F\u002Fdocs.ultralytics.com\u002Fmodels\u002Fyolov3\u002F) to the latest [YOLO26](https:\u002F\u002Fdocs.ultralytics.com\u002Fmodels\u002Fyolo26\u002F). The tables below showcase YOLO26 models pretrained on the [COCO](https:\u002F\u002Fdocs.ultralytics.com\u002Fdatasets\u002Fdetect\u002Fcoco\u002F) dataset for [Detection](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fdetect\u002F), [Segmentation](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fsegment\u002F), and [Pose Estimation](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fpose\u002F). Additionally, [Classification](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fclassify\u002F) models pretrained on the [ImageNet](https:\u002F\u002Fdocs.ultralytics.com\u002Fdatasets\u002Fclassify\u002Fimagenet\u002F) dataset are available. [Tracking](https:\u002F\u002Fdocs.ultralytics.com\u002Fmodes\u002Ftrack\u002F) mode is compatible with all Detection, Segmentation, and Pose models. All [Models](https:\u002F\u002Fdocs.ultralytics.com\u002Fmodels\u002F) are automatically downloaded from the latest Ultralytics [release](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases) upon first use.\n\n\u003Ca href=\"https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002F\" target=\"_blank\">\n    \u003Cimg width=\"100%\" src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fdocs\u002Freleases\u002Fdownload\u002F0\u002Fultralytics-yolov8-tasks-banner.avif\" alt=\"Ultralytics YOLO supported tasks\">\n\u003C\u002Fa>\n\u003Cbr>\n\u003Cbr>\n\n\u003Cdetails open>\u003Csummary>Detection (COCO)\u003C\u002Fsummary>\n\nExplore the [Detection Docs](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fdetect\u002F) for usage examples. These models are trained on the [COCO dataset](https:\u002F\u002Fcocodataset.org\u002F), featuring 80 object classes.\n\n| Model                                                                                | size\u003Cbr>\u003Csup>(pixels)\u003C\u002Fsup> | mAP\u003Csup>val\u003Cbr>50-95\u003C\u002Fsup> | mAP\u003Csup>val\u003Cbr>50-95(e2e)\u003C\u002Fsup> | Speed\u003Cbr>\u003Csup>CPU ONNX\u003Cbr>(ms)\u003C\u002Fsup> | Speed\u003Cbr>\u003Csup>T4 TensorRT10\u003Cbr>(ms)\u003C\u002Fsup> | params\u003Cbr>\u003Csup>(M)\u003C\u002Fsup> | FLOPs\u003Cbr>\u003Csup>(B)\u003C\u002Fsup> |\n| ------------------------------------------------------------------------------------ | --------------------------- | -------------------------- | ------------------------------- | ------------------------------------ | ----------------------------------------- | ------------------------ | ----------------------- |\n| [YOLO26n](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26n.pt) | 640                         | 40.9                       | 40.1                            | 38.9 ± 0.7                           | 1.7 ± 0.0                                 | 2.4                      | 5.4                     |\n| [YOLO26s](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26s.pt) | 640                         | 48.6                       | 47.8                            | 87.2 ± 0.9                           | 2.5 ± 0.0                                 | 9.5                      | 20.7                    |\n| [YOLO26m](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26m.pt) | 640                         | 53.1                       | 52.5                            | 220.0 ± 1.4                          | 4.7 ± 0.1                                 | 20.4                     | 68.2                    |\n| [YOLO26l](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26l.pt) | 640                         | 55.0                       | 54.4                            | 286.2 ± 2.0                          | 6.2 ± 0.2                                 | 24.8                     | 86.4                    |\n| [YOLO26x](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26x.pt) | 640                         | 57.5                       | 56.9                            | 525.8 ± 4.0                          | 11.8 ± 0.2                                | 55.7                     | 193.9                   |\n\n- **mAP\u003Csup>val\u003C\u002Fsup>** values refer to single-model single-scale performance on the [COCO val2017](https:\u002F\u002Fcocodataset.org\u002F) dataset. See [YOLO Performance Metrics](https:\u002F\u002Fdocs.ultralytics.com\u002Fguides\u002Fyolo-performance-metrics\u002F) for details. \u003Cbr>Reproduce with `yolo val detect data=coco.yaml device=0`\n- **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https:\u002F\u002Faws.amazon.com\u002Fec2\u002Finstance-types\u002Fp4\u002F) instance. CPU speeds measured with [ONNX](https:\u002F\u002Fonnx.ai\u002F) export. GPU speeds measured with [TensorRT](https:\u002F\u002Fdeveloper.nvidia.com\u002Ftensorrt) export. \u003Cbr>Reproduce with `yolo val detect data=coco.yaml batch=1 device=0|cpu`\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\u003Csummary>Segmentation (COCO)\u003C\u002Fsummary>\n\nRefer to the [Segmentation Docs](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fsegment\u002F) for usage examples. These models are trained on [COCO-Seg](https:\u002F\u002Fdocs.ultralytics.com\u002Fdatasets\u002Fsegment\u002Fcoco\u002F), including 80 classes.\n\n| Model                                                                                        | size\u003Cbr>\u003Csup>(pixels)\u003C\u002Fsup> | mAP\u003Csup>box\u003Cbr>50-95(e2e)\u003C\u002Fsup> | mAP\u003Csup>mask\u003Cbr>50-95(e2e)\u003C\u002Fsup> | Speed\u003Cbr>\u003Csup>CPU ONNX\u003Cbr>(ms)\u003C\u002Fsup> | Speed\u003Cbr>\u003Csup>T4 TensorRT10\u003Cbr>(ms)\u003C\u002Fsup> | params\u003Cbr>\u003Csup>(M)\u003C\u002Fsup> | FLOPs\u003Cbr>\u003Csup>(B)\u003C\u002Fsup> |\n| -------------------------------------------------------------------------------------------- | --------------------------- | ------------------------------- | -------------------------------- | ------------------------------------ | ----------------------------------------- | ------------------------ | ----------------------- |\n| [YOLO26n-seg](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26n-seg.pt) | 640                         | 39.6                            | 33.9                             | 53.3 ± 0.5                           | 2.1 ± 0.0                                 | 2.7                      | 9.1                     |\n| [YOLO26s-seg](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26s-seg.pt) | 640                         | 47.3                            | 40.0                             | 118.4 ± 0.9                          | 3.3 ± 0.0                                 | 10.4                     | 34.2                    |\n| [YOLO26m-seg](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26m-seg.pt) | 640                         | 52.5                            | 44.1                             | 328.2 ± 2.4                          | 6.7 ± 0.1                                 | 23.6                     | 121.5                   |\n| [YOLO26l-seg](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26l-seg.pt) | 640                         | 54.4                            | 45.5                             | 387.0 ± 3.7                          | 8.0 ± 0.1                                 | 28.0                     | 139.8                   |\n| [YOLO26x-seg](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26x-seg.pt) | 640                         | 56.5                            | 47.0                             | 787.0 ± 6.8                          | 16.4 ± 0.1                                | 62.8                     | 313.5                   |\n\n- **mAP\u003Csup>val\u003C\u002Fsup>** values are for single-model single-scale on the [COCO val2017](https:\u002F\u002Fcocodataset.org\u002F) dataset. See [YOLO Performance Metrics](https:\u002F\u002Fdocs.ultralytics.com\u002Fguides\u002Fyolo-performance-metrics\u002F) for details. \u003Cbr>Reproduce with `yolo val segment data=coco.yaml device=0`\n- **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https:\u002F\u002Faws.amazon.com\u002Fec2\u002Finstance-types\u002Fp4\u002F) instance. CPU speeds measured with [ONNX](https:\u002F\u002Fonnx.ai\u002F) export. GPU speeds measured with [TensorRT](https:\u002F\u002Fdeveloper.nvidia.com\u002Ftensorrt) export. \u003Cbr>Reproduce with `yolo val segment data=coco.yaml batch=1 device=0|cpu`\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\u003Csummary>Classification (ImageNet)\u003C\u002Fsummary>\n\nConsult the [Classification Docs](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fclassify\u002F) for usage examples. These models are trained on [ImageNet](https:\u002F\u002Fdocs.ultralytics.com\u002Fdatasets\u002Fclassify\u002Fimagenet\u002F), covering 1000 classes.\n\n| Model                                                                                        | size\u003Cbr>\u003Csup>(pixels)\u003C\u002Fsup> | acc\u003Cbr>\u003Csup>top1\u003C\u002Fsup> | acc\u003Cbr>\u003Csup>top5\u003C\u002Fsup> | Speed\u003Cbr>\u003Csup>CPU ONNX\u003Cbr>(ms)\u003C\u002Fsup> | Speed\u003Cbr>\u003Csup>T4 TensorRT10\u003Cbr>(ms)\u003C\u002Fsup> | params\u003Cbr>\u003Csup>(M)\u003C\u002Fsup> | FLOPs\u003Cbr>\u003Csup>(B) at 224\u003C\u002Fsup> |\n| -------------------------------------------------------------------------------------------- | --------------------------- | ---------------------- | ---------------------- | ------------------------------------ | ----------------------------------------- | ------------------------ | ------------------------------ |\n| [YOLO26n-cls](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26n-cls.pt) | 224                         | 71.4                   | 90.1                   | 5.0 ± 0.3                            | 1.1 ± 0.0                                 | 2.8                      | 0.5                            |\n| [YOLO26s-cls](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26s-cls.pt) | 224                         | 76.0                   | 92.9                   | 7.9 ± 0.2                            | 1.3 ± 0.0                                 | 6.7                      | 1.6                            |\n| [YOLO26m-cls](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26m-cls.pt) | 224                         | 78.1                   | 94.2                   | 17.2 ± 0.4                           | 2.0 ± 0.0                                 | 11.6                     | 4.9                            |\n| [YOLO26l-cls](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26l-cls.pt) | 224                         | 79.0                   | 94.6                   | 23.2 ± 0.3                           | 2.8 ± 0.0                                 | 14.1                     | 6.2                            |\n| [YOLO26x-cls](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26x-cls.pt) | 224                         | 79.9                   | 95.0                   | 41.4 ± 0.9                           | 3.8 ± 0.0                                 | 29.6                     | 13.6                           |\n\n- **acc** values represent model accuracy on the [ImageNet](https:\u002F\u002Fwww.image-net.org\u002F) dataset validation set. \u003Cbr>Reproduce with `yolo val classify data=path\u002Fto\u002FImageNet device=0`\n- **Speed** metrics are averaged over ImageNet val images using an [Amazon EC2 P4d](https:\u002F\u002Faws.amazon.com\u002Fec2\u002Finstance-types\u002Fp4\u002F) instance. CPU speeds measured with [ONNX](https:\u002F\u002Fonnx.ai\u002F) export. GPU speeds measured with [TensorRT](https:\u002F\u002Fdeveloper.nvidia.com\u002Ftensorrt) export. \u003Cbr>Reproduce with `yolo val classify data=path\u002Fto\u002FImageNet batch=1 device=0|cpu`\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\u003Csummary>Pose (COCO)\u003C\u002Fsummary>\n\nSee the [Pose Estimation Docs](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fpose\u002F) for usage examples. These models are trained on [COCO-Pose](https:\u002F\u002Fdocs.ultralytics.com\u002Fdatasets\u002Fpose\u002Fcoco\u002F), focusing on the 'person' class.\n\n| Model                                                                                          | size\u003Cbr>\u003Csup>(pixels)\u003C\u002Fsup> | mAP\u003Csup>pose\u003Cbr>50-95(e2e)\u003C\u002Fsup> | mAP\u003Csup>pose\u003Cbr>50(e2e)\u003C\u002Fsup> | Speed\u003Cbr>\u003Csup>CPU ONNX\u003Cbr>(ms)\u003C\u002Fsup> | Speed\u003Cbr>\u003Csup>T4 TensorRT10\u003Cbr>(ms)\u003C\u002Fsup> | params\u003Cbr>\u003Csup>(M)\u003C\u002Fsup> | FLOPs\u003Cbr>\u003Csup>(B)\u003C\u002Fsup> |\n| ---------------------------------------------------------------------------------------------- | --------------------------- | -------------------------------- | ----------------------------- | ------------------------------------ | ----------------------------------------- | ------------------------ | ----------------------- |\n| [YOLO26n-pose](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26n-pose.pt) | 640                         | 57.2                             | 83.3                          | 40.3 ± 0.5                           | 1.8 ± 0.0                                 | 2.9                      | 7.5                     |\n| [YOLO26s-pose](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26s-pose.pt) | 640                         | 63.0                             | 86.6                          | 85.3 ± 0.9                           | 2.7 ± 0.0                                 | 10.4                     | 23.9                    |\n| [YOLO26m-pose](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26m-pose.pt) | 640                         | 68.8                             | 89.6                          | 218.0 ± 1.5                          | 5.0 ± 0.1                                 | 21.5                     | 73.1                    |\n| [YOLO26l-pose](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26l-pose.pt) | 640                         | 70.4                             | 90.5                          | 275.4 ± 2.4                          | 6.5 ± 0.1                                 | 25.9                     | 91.3                    |\n| [YOLO26x-pose](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26x-pose.pt) | 640                         | 71.6                             | 91.6                          | 565.4 ± 3.0                          | 12.2 ± 0.2                                | 57.6                     | 201.7                   |\n\n- **mAP\u003Csup>val\u003C\u002Fsup>** values are for single-model single-scale on the [COCO Keypoints val2017](https:\u002F\u002Fdocs.ultralytics.com\u002Fdatasets\u002Fpose\u002Fcoco\u002F) dataset. See [YOLO Performance Metrics](https:\u002F\u002Fdocs.ultralytics.com\u002Fguides\u002Fyolo-performance-metrics\u002F) for details. \u003Cbr>Reproduce with `yolo val pose data=coco-pose.yaml device=0`\n- **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https:\u002F\u002Faws.amazon.com\u002Fec2\u002Finstance-types\u002Fp4\u002F) instance. CPU speeds measured with [ONNX](https:\u002F\u002Fonnx.ai\u002F) export. GPU speeds measured with [TensorRT](https:\u002F\u002Fdeveloper.nvidia.com\u002Ftensorrt) export. \u003Cbr>Reproduce with `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\u003Csummary>Oriented Bounding Boxes (DOTAv1)\u003C\u002Fsummary>\n\nCheck the [OBB Docs](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fobb\u002F) for usage examples. These models are trained on [DOTAv1](https:\u002F\u002Fdocs.ultralytics.com\u002Fdatasets\u002Fobb\u002Fdota-v2\u002F#dota-v10\u002F), including 15 classes.\n\n| Model                                                                                        | size\u003Cbr>\u003Csup>(pixels)\u003C\u002Fsup> | mAP\u003Csup>test\u003Cbr>50-95(e2e)\u003C\u002Fsup> | mAP\u003Csup>test\u003Cbr>50(e2e)\u003C\u002Fsup> | Speed\u003Cbr>\u003Csup>CPU ONNX\u003Cbr>(ms)\u003C\u002Fsup> | Speed\u003Cbr>\u003Csup>T4 TensorRT10\u003Cbr>(ms)\u003C\u002Fsup> | params\u003Cbr>\u003Csup>(M)\u003C\u002Fsup> | FLOPs\u003Cbr>\u003Csup>(B)\u003C\u002Fsup> |\n| -------------------------------------------------------------------------------------------- | --------------------------- | -------------------------------- | ----------------------------- | ------------------------------------ | ----------------------------------------- | ------------------------ | ----------------------- |\n| [YOLO26n-obb](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26n-obb.pt) | 1024                        | 52.4                             | 78.9                          | 97.7 ± 0.9                           | 2.8 ± 0.0                                 | 2.5                      | 14.0                    |\n| [YOLO26s-obb](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26s-obb.pt) | 1024                        | 54.8                             | 80.9                          | 218.0 ± 1.4                          | 4.9 ± 0.1                                 | 9.8                      | 55.1                    |\n| [YOLO26m-obb](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26m-obb.pt) | 1024                        | 55.3                             | 81.0                          | 579.2 ± 3.8                          | 10.2 ± 0.3                                | 21.2                     | 183.3                   |\n| [YOLO26l-obb](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26l-obb.pt) | 1024                        | 56.2                             | 81.6                          | 735.6 ± 3.1                          | 13.0 ± 0.2                                | 25.6                     | 230.0                   |\n| [YOLO26x-obb](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv8.4.0\u002Fyolo26x-obb.pt) | 1024                        | 56.7                             | 81.7                          | 1485.7 ± 11.5                        | 30.5 ± 0.9                                | 57.6                     | 516.5                   |\n\n- **mAP\u003Csup>test\u003C\u002Fsup>** values are for single-model multiscale performance on the [DOTAv1 test set](https:\u002F\u002Fcaptain-whu.github.io\u002FDOTA\u002Fdataset.html). \u003Cbr>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to the [DOTA evaluation server](https:\u002F\u002Fcaptain-whu.github.io\u002FDOTA\u002Fevaluation.html).\n- **Speed** metrics are averaged over [DOTAv1 val images](https:\u002F\u002Fdocs.ultralytics.com\u002Fdatasets\u002Fobb\u002Fdota-v2\u002F#dota-v10) using an [Amazon EC2 P4d](https:\u002F\u002Faws.amazon.com\u002Fec2\u002Finstance-types\u002Fp4\u002F) instance. CPU speeds measured with [ONNX](https:\u002F\u002Fonnx.ai\u002F) export. GPU speeds measured with [TensorRT](https:\u002F\u002Fdeveloper.nvidia.com\u002Ftensorrt) export. \u003Cbr>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`\n\n\u003C\u002Fdetails>\n\n## 🧩 Integrations\n\nOur key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with partners like [Weights & Biases](https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fweights-biases\u002F), [Comet ML](https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fcomet\u002F), [Roboflow](https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Froboflow\u002F), and [Intel OpenVINO](https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fopenvino\u002F), can optimize your AI workflow. Explore more at [Ultralytics Integrations](https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002F).\n\n\u003Ca href=\"https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002F\" target=\"_blank\">\n    \u003Cimg width=\"100%\" src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fyolov8\u002Fbanner-integrations.png\" alt=\"Ultralytics active learning integrations\">\n\u003C\u002Fa>\n\u003Cbr>\n\u003Cbr>\n\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fplatform.ultralytics.com\u002Fultralytics\u002Fyolo26\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fpartners\u002Flogo-ultralytics-hub.png\" width=\"10%\" alt=\"Ultralytics Platform logo\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"15%\" height=\"0\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fweights-biases\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fpartners\u002Flogo-wb.png\" width=\"10%\" alt=\"Weights & Biases logo\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"15%\" height=\"0\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fcomet\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fpartners\u002Flogo-comet.png\" width=\"10%\" alt=\"Comet ML logo\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"15%\" height=\"0\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fneural-magic\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fpartners\u002Flogo-neuralmagic.png\" width=\"10%\" alt=\"Neural Magic logo\">\u003C\u002Fa>\n\u003C\u002Fdiv>\n\n|                                                                   Ultralytics Platform 🌟                                                                   |                                                          Weights & Biases                                                           |                                                                              Comet                                                                              |                                                        Neural Magic                                                         |\n| :---------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: |\n| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics Platform](https:\u002F\u002Fplatform.ultralytics.com\u002Fultralytics\u002Fyolo26). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fweights-biases\u002F). | Free forever, [Comet ML](https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fcomet\u002F) lets you save YOLO models, resume training, and interactively visualize predictions. | Run YOLO inference up to 6x faster with [Neural Magic DeepSparse](https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fneural-magic\u002F). |\n\n## 🤝 Contribute\n\nWe thrive on community collaboration! Ultralytics YOLO wouldn't be the SOTA framework it is without contributions from developers like you. Please see our [Contributing Guide](https:\u002F\u002Fdocs.ultralytics.com\u002Fhelp\u002Fcontributing\u002F) to get started. We also welcome your feedback—share your experience by completing our [Survey](https:\u002F\u002Fwww.ultralytics.com\u002Fsurvey?utm_source=github&utm_medium=social&utm_campaign=Survey). A huge **Thank You** 🙏 to everyone who contributes!\n\n\u003C!-- SVG image from https:\u002F\u002Fopencollective.com\u002Fultralytics\u002Fcontributors.svg?width=1280 -->\n\n[![Ultralytics open-source contributors](https:\u002F\u002Fraw.githubusercontent.com\u002Fultralytics\u002Fassets\u002Fmain\u002Fim\u002Fimage-contributors.png)](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fultralytics\u002Fgraphs\u002Fcontributors)\n\nWe look forward to your contributions to help make the Ultralytics ecosystem even better!\n\n## 📜 License\n\nUltralytics offers two licensing options to suit different needs:\n\n- **AGPL-3.0 License**: This [OSI-approved](https:\u002F\u002Fopensource.org\u002Flicense\u002Fagpl-3.0) open-source license is perfect for students, researchers, and enthusiasts. It encourages open collaboration and knowledge sharing. See the [LICENSE](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fultralytics\u002Fblob\u002Fmain\u002FLICENSE) file for full details.\n- **Ultralytics Enterprise License**: Designed for commercial use, this license allows for the seamless integration of Ultralytics software and AI models into commercial products and services, bypassing the open-source requirements of AGPL-3.0. If your use case involves commercial deployment, please contact us via [Ultralytics Licensing](https:\u002F\u002Fwww.ultralytics.com\u002Flicense).\n\n## 📞 Contact\n\nFor bug reports and feature requests related to Ultralytics software, please visit [GitHub Issues](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fultralytics\u002Fissues). For questions, discussions, and community support, join our active communities on [Discord](https:\u002F\u002Fdiscord.com\u002Finvite\u002Fultralytics), [Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Fultralytics\u002F), and the [Ultralytics Community Forums](https:\u002F\u002Fcommunity.ultralytics.com\u002F). We're here to help with all things Ultralytics!\n\n\u003Cbr>\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fultralytics\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-github.png\" width=\"3%\" alt=\"Ultralytics GitHub\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fultralytics\u002F\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-linkedin.png\" width=\"3%\" alt=\"Ultralytics LinkedIn\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Ftwitter.com\u002Fultralytics\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-twitter.png\" width=\"3%\" alt=\"Ultralytics Twitter\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fultralytics?sub_confirmation=1\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-youtube.png\" width=\"3%\" alt=\"Ultralytics YouTube\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fwww.tiktok.com\u002F@ultralytics\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-tiktok.png\" width=\"3%\" alt=\"Ultralytics TikTok\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fultralytics.com\u002Fbilibili\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-bilibili.png\" width=\"3%\" alt=\"Ultralytics BiliBili\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fdiscord.com\u002Finvite\u002Fultralytics\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-discord.png\" width=\"3%\" alt=\"Ultralytics Discord\">\u003C\u002Fa>\n\u003C\u002Fdiv>\n","Ultralytics YOLO 是一个用于目标检测、图像分类、实例分割等计算机视觉任务的高性能深度学习框架。它基于 PyTorch 构建，提供了一系列先进的 YOLO 模型，具有快速、准确和易用的特点。该项目支持多种功能，包括但不限于对象检测、图像分类、姿态估计以及跟踪等，并且可以通过命令行界面轻松使用。适用于需要实时处理大量视觉数据的应用场景，如安防监控、自动驾驶、工业检测等领域。",2,"2026-06-11 02:37:36","top_all"]