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Update YOLO11 notebooks (#16608)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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  1. 26
      examples/heatmaps.ipynb
  2. 2
      examples/hub.ipynb
  3. 30
      examples/object_counting.ipynb
  4. 22
      examples/object_tracking.ipynb
  5. 104
      examples/tutorial.ipynb

26
examples/heatmaps.ipynb
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@ -19,11 +19,11 @@
" <a href=\"https://www.kaggle.com/ultralytics/yolov8\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
" <a href=\"https://ultralytics.com/discord\"><img alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\"></a>\n",
"\n",
"Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and understand its features and capabilities.\n",
"Welcome to the Ultralytics YOLO11 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLO11</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLO11 and understand its features and capabilities.\n",
"\n",
"YOLOv8 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
"YOLO11 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
"\n",
"We hope that the resources in this notebook will help you get the most out of YOLOv8. Please browse the YOLOv8 <a href=\"https://docs.ultralytics.com/guides/heatmaps\">Heatmap Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
"We hope that the resources in this notebook will help you get the most out of YOLO11. Please browse the YOLO11 <a href=\"https://docs.ultralytics.com/guides/heatmaps\">Heatmap Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
"\n",
"</div>"
]
@ -56,7 +56,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Ultralytics YOLOv8.2.17 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"Ultralytics 8.2.17 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 29.8/78.2 GB disk)\n"
]
}
@ -76,14 +76,14 @@
"source": [
"# Introduction to Heatmaps\n",
"\n",
"A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) transforms complex data into a vibrant, color-coded matrix. This visual tool employs a spectrum of colors to represent varying data values, where warmer hues indicate higher intensities and cooler tones signify lower values. Heatmaps excel in visualizing intricate data patterns, correlations, and anomalies, offering an accessible and engaging approach to data interpretation across diverse domains.\n",
"A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) transforms complex data into a vibrant, color-coded matrix. This visual tool employs a spectrum of colors to represent varying data values, where warmer hues indicate higher intensities and cooler tones signify lower values. Heatmaps excel in visualizing intricate data patterns, correlations, and anomalies, offering an accessible and engaging approach to data interpretation across diverse domains.\n",
"\n",
"## Real World Applications\n",
"\n",
"| Transportation | Retail |\n",
"|:-----------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------:|\n",
"| ![Ultralytics YOLOv8 Transportation Heatmap](https://github.com/RizwanMunawar/ultralytics/assets/62513924/288d7053-622b-4452-b4e4-1f41aeb764aa) | ![Ultralytics YOLOv8 Retail Heatmap](https://github.com/RizwanMunawar/ultralytics/assets/62513924/edef75ad-50a7-4c0a-be4a-a66cdfc12802) |\n",
"| Ultralytics YOLOv8 Transportation Heatmap | Ultralytics YOLOv8 Retail Heatmap |\n"
"| ![Ultralytics YOLO11 Transportation Heatmap](https://github.com/RizwanMunawar/ultralytics/assets/62513924/288d7053-622b-4452-b4e4-1f41aeb764aa) | ![Ultralytics YOLO11 Retail Heatmap](https://github.com/RizwanMunawar/ultralytics/assets/62513924/edef75ad-50a7-4c0a-be4a-a66cdfc12802) |\n",
"| Ultralytics YOLO11 Transportation Heatmap | Ultralytics YOLO11 Retail Heatmap |\n"
]
},
{
@ -99,7 +99,7 @@
"from ultralytics import YOLO, solutions\n",
"\n",
"# Load YOLO model\n",
"model = YOLO(\"yolov8n.pt\")\n",
"model = YOLO(\"yolo11n.pt\")\n",
"\n",
"# Open video file\n",
"cap = cv2.VideoCapture(\"path/to/video/file.mp4\")\n",
@ -161,15 +161,15 @@
"- [About Us](https://ultralytics.com/about): Discover our mission, vision, and the story behind Ultralytics.\n",
"- [Join Our Team](https://ultralytics.com/work): Explore career opportunities and join our team of talented professionals.\n",
"\n",
"## YOLOv8 🚀 Resources\n",
"## YOLO11 🚀 Resources\n",
"\n",
"YOLOv8 is the latest evolution in the YOLO series, offering state-of-the-art performance in object detection and image segmentation. Here are some essential resources to help you get started with YOLOv8:\n",
"YOLO11 is the latest evolution in the YOLO series, offering state-of-the-art performance in object detection and image segmentation. Here are some essential resources to help you get started with YOLO11:\n",
"\n",
"- [GitHub](https://github.com/ultralytics/ultralytics): Access the YOLOv8 repository on GitHub, where you can find the source code, contribute to the project, and report issues.\n",
"- [Docs](https://docs.ultralytics.com/): Explore the official documentation for YOLOv8, including installation guides, tutorials, and detailed API references.\n",
"- [GitHub](https://github.com/ultralytics/ultralytics): Access the YOLO11 repository on GitHub, where you can find the source code, contribute to the project, and report issues.\n",
"- [Docs](https://docs.ultralytics.com/): Explore the official documentation for YOLO11, including installation guides, tutorials, and detailed API references.\n",
"- [Discord](https://ultralytics.com/discord): Join our Discord community to connect with other users, share your projects, and get help from the Ultralytics team.\n",
"\n",
"These resources are designed to help you leverage the full potential of Ultralytics' offerings and YOLOv8. Whether you're a beginner or an experienced developer, you'll find the information and support you need to succeed."
"These resources are designed to help you leverage the full potential of Ultralytics' offerings and YOLO11. Whether you're a beginner or an experienced developer, you'll find the information and support you need to succeed."
]
}
],

2
examples/hub.ipynb
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@ -54,7 +54,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"Ultralytics 8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 28.8/78.2 GB disk)\n"
]
}

30
examples/object_counting.ipynb
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@ -19,11 +19,11 @@
" <a href=\"https://www.kaggle.com/ultralytics/yolov8\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
" <a href=\"https://ultralytics.com/discord\"><img alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\"></a>\n",
"\n",
"Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and understand its features and capabilities.\n",
"Welcome to the Ultralytics YOLO11 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLO11</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLO11 and understand its features and capabilities.\n",
"\n",
"YOLOv8 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
"YOLO11 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
"\n",
"We hope that the resources in this notebook will help you get the most out of YOLOv8. Please browse the YOLOv8 <a href=\"https://docs.ultralytics.com/guides/object-counting/\"> Object Counting Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
"We hope that the resources in this notebook will help you get the most out of YOLO11. Please browse the YOLO11 <a href=\"https://docs.ultralytics.com/guides/object-counting/\"> Object Counting Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
"\n",
"</div>"
]
@ -56,7 +56,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Ultralytics YOLOv8.2.18 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"Ultralytics 8.2.18 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 29.8/78.2 GB disk)\n"
]
}
@ -74,11 +74,11 @@
"id": "m7VkxQ2aeg7k"
},
"source": [
"# Object Counting using Ultralytics YOLOv8 🚀\n",
"# Object Counting using Ultralytics YOLO11 🚀\n",
"\n",
"## What is Object Counting?\n",
"\n",
"Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves accurate identification and counting of specific objects in videos and camera streams. YOLOv8 excels in real-time applications, providing efficient and precise object counting for various scenarios like crowd analysis and surveillance, thanks to its state-of-the-art algorithms and deep learning capabilities.\n",
"Object counting with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) involves accurate identification and counting of specific objects in videos and camera streams. YOLO11 excels in real-time applications, providing efficient and precise object counting for various scenarios like crowd analysis and surveillance, thanks to its state-of-the-art algorithms and deep learning capabilities.\n",
"\n",
"## Advantages of Object Counting?\n",
"\n",
@ -90,8 +90,8 @@
"\n",
"| Logistics | Aquaculture |\n",
"|:-------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------:|\n",
"| ![Conveyor Belt Packets Counting Using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/70e2d106-510c-4c6c-a57a-d34a765aa757) | ![Fish Counting in Sea using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/c60d047b-3837-435f-8d29-bb9fc95d2191) |\n",
"| Conveyor Belt Packets Counting Using Ultralytics YOLOv8 | Fish Counting in Sea using Ultralytics YOLOv8 |\n"
"| ![Conveyor Belt Packets Counting Using Ultralytics YOLO11](https://github.com/RizwanMunawar/ultralytics/assets/62513924/70e2d106-510c-4c6c-a57a-d34a765aa757) | ![Fish Counting in Sea using Ultralytics YOLO11](https://github.com/RizwanMunawar/ultralytics/assets/62513924/c60d047b-3837-435f-8d29-bb9fc95d2191) |\n",
"| Conveyor Belt Packets Counting Using Ultralytics YOLO11 | Fish Counting in Sea using Ultralytics YOLO11 |\n"
]
},
{
@ -106,8 +106,8 @@
"\n",
"from ultralytics import YOLO, solutions\n",
"\n",
"# Load the pre-trained YOLOv8 model\n",
"model = YOLO(\"yolov8n.pt\")\n",
"# Load the pre-trained YOLO11 model\n",
"model = YOLO(\"yolo11n.pt\")\n",
"\n",
"# Open the video file\n",
"cap = cv2.VideoCapture(\"path/to/video/file.mp4\")\n",
@ -179,15 +179,15 @@
"- [About Us](https://ultralytics.com/about): Discover our mission, vision, and the story behind Ultralytics.\n",
"- [Join Our Team](https://ultralytics.com/work): Explore career opportunities and join our team of talented professionals.\n",
"\n",
"## YOLOv8 🚀 Resources\n",
"## YOLO11 🚀 Resources\n",
"\n",
"YOLOv8 is the latest evolution in the YOLO series, offering state-of-the-art performance in object detection and image segmentation. Here are some essential resources to help you get started with YOLOv8:\n",
"YOLO11 is the latest evolution in the YOLO series, offering state-of-the-art performance in object detection and image segmentation. Here are some essential resources to help you get started with YOLO11:\n",
"\n",
"- [GitHub](https://github.com/ultralytics/ultralytics): Access the YOLOv8 repository on GitHub, where you can find the source code, contribute to the project, and report issues.\n",
"- [Docs](https://docs.ultralytics.com/): Explore the official documentation for YOLOv8, including installation guides, tutorials, and detailed API references.\n",
"- [GitHub](https://github.com/ultralytics/ultralytics): Access the YOLO11 repository on GitHub, where you can find the source code, contribute to the project, and report issues.\n",
"- [Docs](https://docs.ultralytics.com/): Explore the official documentation for YOLO11, including installation guides, tutorials, and detailed API references.\n",
"- [Discord](https://ultralytics.com/discord): Join our Discord community to connect with other users, share your projects, and get help from the Ultralytics team.\n",
"\n",
"These resources are designed to help you leverage the full potential of Ultralytics' offerings and YOLOv8. Whether you're a beginner or an experienced developer, you'll find the information and support you need to succeed."
"These resources are designed to help you leverage the full potential of Ultralytics' offerings and YOLO11. Whether you're a beginner or an experienced developer, you'll find the information and support you need to succeed."
]
}
],

22
examples/object_tracking.ipynb
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@ -19,11 +19,11 @@
" <a href=\"https://www.kaggle.com/ultralytics/yolov8\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
" <a href=\"https://ultralytics.com/discord\"><img alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\"></a>\n",
"\n",
"Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and understand its features and capabilities.\n",
"Welcome to the Ultralytics YOLO11 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLO11</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLO11 and understand its features and capabilities.\n",
"\n",
"YOLOv8 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
"YOLO11 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
"\n",
"We hope that the resources in this notebook will help you get the most out of YOLOv8. Please browse the YOLOv8 <a href=\"https://docs.ultralytics.com/modes/track/\"> Tracking Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
"We hope that the resources in this notebook will help you get the most out of YOLO11. Please browse the YOLO11 <a href=\"https://docs.ultralytics.com/modes/track/\"> Tracking Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
"\n",
"</div>"
]
@ -56,7 +56,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Ultralytics YOLOv8.2.17 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"Ultralytics 8.2.17 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 29.8/78.2 GB disk)\n"
]
}
@ -76,7 +76,7 @@
"source": [
"# Ultralytics Object Tracking\n",
"\n",
"[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) instance segmentation involves identifying and outlining individual objects in an image, providing a detailed understanding of spatial distribution. Unlike semantic segmentation, it uniquely labels and precisely delineates each object, crucial for tasks like object detection and medical imaging.\n",
"[Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) instance segmentation involves identifying and outlining individual objects in an image, providing a detailed understanding of spatial distribution. Unlike semantic segmentation, it uniquely labels and precisely delineates each object, crucial for tasks like object detection and medical imaging.\n",
"\n",
"There are two types of instance segmentation tracking available in the Ultralytics package:\n",
"\n",
@ -144,7 +144,7 @@
"track_history = defaultdict(lambda: [])\n",
"\n",
"# Load the YOLO model with segmentation capabilities\n",
"model = YOLO(\"yolov8n-seg.pt\")\n",
"model = YOLO(\"yolo11n-seg.pt\")\n",
"\n",
"# Open the video file\n",
"cap = cv2.VideoCapture(\"path/to/video/file.mp4\")\n",
@ -214,15 +214,15 @@
"- [About Us](https://ultralytics.com/about): Discover our mission, vision, and the story behind Ultralytics.\n",
"- [Join Our Team](https://ultralytics.com/work): Explore career opportunities and join our team of talented professionals.\n",
"\n",
"## YOLOv8 🚀 Resources\n",
"## YOLO11 🚀 Resources\n",
"\n",
"YOLOv8 is the latest evolution in the YOLO series, offering state-of-the-art performance in object detection and image segmentation. Here are some essential resources to help you get started with YOLOv8:\n",
"YOLO11 is the latest evolution in the YOLO series, offering state-of-the-art performance in object detection and image segmentation. Here are some essential resources to help you get started with YOLO11:\n",
"\n",
"- [GitHub](https://github.com/ultralytics/ultralytics): Access the YOLOv8 repository on GitHub, where you can find the source code, contribute to the project, and report issues.\n",
"- [Docs](https://docs.ultralytics.com/): Explore the official documentation for YOLOv8, including installation guides, tutorials, and detailed API references.\n",
"- [GitHub](https://github.com/ultralytics/ultralytics): Access the YOLO11 repository on GitHub, where you can find the source code, contribute to the project, and report issues.\n",
"- [Docs](https://docs.ultralytics.com/): Explore the official documentation for YOLO11, including installation guides, tutorials, and detailed API references.\n",
"- [Discord](https://ultralytics.com/discord): Join our Discord community to connect with other users, share your projects, and get help from the Ultralytics team.\n",
"\n",
"These resources are designed to help you leverage the full potential of Ultralytics' offerings and YOLOv8. Whether you're a beginner or an experienced developer, you'll find the information and support you need to succeed."
"These resources are designed to help you leverage the full potential of Ultralytics' offerings and YOLO11. Whether you're a beginner or an experienced developer, you'll find the information and support you need to succeed."
]
}
],

104
examples/tutorial.ipynb
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@ -3,7 +3,7 @@
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "YOLOv8 Tutorial",
"name": "YOLO11 Tutorial",
"provenance": [],
"toc_visible": true
},
@ -36,11 +36,11 @@
" <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>\n",
" <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>\n",
"\n",
"Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and understand its features and capabilities.\n",
"Welcome to the Ultralytics YOLO11 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLO11</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLO11 and understand its features and capabilities.\n",
"\n",
"YOLOv8 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
"YOLO11 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
"\n",
"We hope that the resources in this notebook will help you get the most out of YOLOv8. Please browse the YOLOv8 <a href=\"https://docs.ultralytics.com/\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
"We hope that the resources in this notebook will help you get the most out of YOLO11. Please browse the YOLO11 <a href=\"https://docs.ultralytics.com/\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
"\n",
"</div>"
]
@ -78,7 +78,7 @@
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 28.8/78.2 GB disk)\n"
]
}
@ -92,7 +92,7 @@
"source": [
"# 1. Predict\n",
"\n",
"YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/usage/cfg/) and other details in the [YOLOv8 Predict Docs](https://docs.ultralytics.com/modes/train/).\n"
"YOLO11 may be used directly in the Command Line Interface (CLI) with a `yolo` command for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/usage/cfg/) and other details in the [YOLO11 Predict Docs](https://docs.ultralytics.com/modes/train/).\n"
]
},
{
@ -106,7 +106,7 @@
},
"source": [
"# Run inference on an image with YOLOv8n\n",
"!yolo predict model=yolov8n.pt source='https://ultralytics.com/images/zidane.jpg'"
"!yolo predict model=yolo11n.pt source='https://ultralytics.com/images/zidane.jpg'"
],
"execution_count": null,
"outputs": [
@ -116,7 +116,7 @@
"text": [
"Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n.pt to 'yolov8n.pt'...\n",
"100% 6.23M/6.23M [00:00<00:00, 83.2MB/s]\n",
"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
"\n",
"Downloading https://ultralytics.com/images/zidane.jpg to 'zidane.jpg'...\n",
@ -146,7 +146,7 @@
},
"source": [
"# 2. Val\n",
"Validate a model's accuracy on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset's `val` or `test` splits. The latest YOLOv8 [models](https://github.com/ultralytics/ultralytics#models) are downloaded automatically the first time they are used. See [YOLOv8 Val Docs](https://docs.ultralytics.com/modes/val/) for more information."
"Validate a model's accuracy on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset's `val` or `test` splits. The latest YOLO11 [models](https://github.com/ultralytics/ultralytics#models) are downloaded automatically the first time they are used. See [YOLO11 Val Docs](https://docs.ultralytics.com/modes/val/) for more information."
]
},
{
@ -174,7 +174,7 @@
},
"source": [
"# Validate YOLOv8n on COCO8 val\n",
"!yolo val model=yolov8n.pt data=coco8.yaml"
"!yolo val model=yolo11n.pt data=coco8.yaml"
],
"execution_count": null,
"outputs": [
@ -182,7 +182,7 @@
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
"\n",
"Dataset 'coco8.yaml' images not found ⚠️, missing path '/content/datasets/coco8/images/val'\n",
@ -220,13 +220,13 @@
"\n",
"<p align=\"\"><a href=\"https://ultralytics.com/hub\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\"/></a></p>\n",
"\n",
"Train YOLOv8 on [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/) datasets. See [YOLOv8 Train Docs](https://docs.ultralytics.com/modes/train/) for more information."
"Train YOLO11 on [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/) datasets. See [YOLO11 Train Docs](https://docs.ultralytics.com/modes/train/) for more information."
]
},
{
"cell_type": "code",
"source": [
"#@title Select YOLOv8 🚀 logger {run: 'auto'}\n",
"#@title Select YOLO11 🚀 logger {run: 'auto'}\n",
"logger = 'Comet' #@param ['Comet', 'TensorBoard']\n",
"\n",
"if logger == 'Comet':\n",
@ -253,7 +253,7 @@
},
"source": [
"# Train YOLOv8n on COCO8 for 3 epochs\n",
"!yolo train model=yolov8n.pt data=coco8.yaml epochs=3 imgsz=640"
"!yolo train model=yolo11n.pt data=coco8.yaml epochs=3 imgsz=640"
],
"execution_count": null,
"outputs": [
@ -261,8 +261,8 @@
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=coco8.yaml, epochs=3, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n",
"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolo11n.pt, data=coco8.yaml, epochs=3, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n",
@ -330,7 +330,7 @@
"Optimizer stripped from runs/detect/train/weights/best.pt, 6.5MB\n",
"\n",
"Validating runs/detect/train/weights/best.pt...\n",
"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"Model summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 18.23it/s]\n",
" all 4 17 0.617 0.884 0.888 0.622\n",
@ -352,26 +352,26 @@
"source": [
"# 4. Export\n",
"\n",
"Export a YOLOv8 model to any supported format below with the `format` argument, i.e. `format=onnx`. See [YOLOv8 Export Docs](https://docs.ultralytics.com/modes/export/) for more information.\n",
"Export a YOLO11 model to any supported format below with the `format` argument, i.e. `format=onnx`. See [YOLO11 Export Docs](https://docs.ultralytics.com/modes/export/) for more information.\n",
"\n",
"- 💡 ProTip: Export to [ONNX](https://docs.ultralytics.com/integrations/onnx/) or [OpenVINO](https://docs.ultralytics.com/integrations/openvino/) for up to 3x CPU speedup. \n",
"- 💡 ProTip: Export to [TensorRT](https://docs.ultralytics.com/integrations/tensorrt/) for up to 5x GPU speedup.\n",
"\n",
"| Format | `format` Argument | Model | Metadata | Arguments |\n",
"|--------------------------------------------------------------------------|-------------------|---------------------------|----------|----------------------------------------------------------------------|\n",
"| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |\n",
"| [TorchScript](https://docs.ultralytics.com/integrations/torchscript) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |\n",
"| [ONNX](https://docs.ultralytics.com/integrations/onnx) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |\n",
"| [OpenVINO](https://docs.ultralytics.com/integrations/openvino) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
"| [TensorRT](https://docs.ultralytics.com/integrations/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |\n",
"| [CoreML](https://docs.ultralytics.com/integrations/coreml) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |\n",
"| [TF SavedModel](https://docs.ultralytics.com/integrations/tf-savedmodel) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |\n",
"| [TF GraphDef](https://docs.ultralytics.com/integrations/tf-graphdef) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |\n",
"| [TF Lite](https://docs.ultralytics.com/integrations/tflite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
"| [TF Edge TPU](https://docs.ultralytics.com/integrations/edge-tpu) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` |\n",
"| [TF.js](https://docs.ultralytics.com/integrations/tfjs) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
"| [PaddlePaddle](https://docs.ultralytics.com/integrations/paddlepaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |\n",
"| [NCNN](https://docs.ultralytics.com/integrations/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |"
"| [PyTorch](https://pytorch.org/) | - | `yolo11n.pt` | ✅ | - |\n",
"| [TorchScript](https://docs.ultralytics.com/integrations/torchscript) | `torchscript` | `yolo11n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |\n",
"| [ONNX](https://docs.ultralytics.com/integrations/onnx) | `onnx` | `yolo11n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |\n",
"| [OpenVINO](https://docs.ultralytics.com/integrations/openvino) | `openvino` | `yolo11n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
"| [TensorRT](https://docs.ultralytics.com/integrations/tensorrt) | `engine` | `yolo11n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |\n",
"| [CoreML](https://docs.ultralytics.com/integrations/coreml) | `coreml` | `yolo11n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |\n",
"| [TF SavedModel](https://docs.ultralytics.com/integrations/tf-savedmodel) | `saved_model` | `yolo11n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |\n",
"| [TF GraphDef](https://docs.ultralytics.com/integrations/tf-graphdef) | `pb` | `yolo11n.pb` | ❌ | `imgsz`, `batch` |\n",
"| [TF Lite](https://docs.ultralytics.com/integrations/tflite) | `tflite` | `yolo11n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
"| [TF Edge TPU](https://docs.ultralytics.com/integrations/edge-tpu) | `edgetpu` | `yolo11n_edgetpu.tflite` | ✅ | `imgsz` |\n",
"| [TF.js](https://docs.ultralytics.com/integrations/tfjs) | `tfjs` | `yolo11n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
"| [PaddlePaddle](https://docs.ultralytics.com/integrations/paddlepaddle) | `paddle` | `yolo11n_paddle_model/` | ✅ | `imgsz`, `batch` |\n",
"| [NCNN](https://docs.ultralytics.com/integrations/ncnn) | `ncnn` | `yolo11n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |"
],
"metadata": {
"id": "nPZZeNrLCQG6"
@ -380,7 +380,7 @@
{
"cell_type": "code",
"source": [
"!yolo export model=yolov8n.pt format=torchscript"
"!yolo export model=yolo11n.pt format=torchscript"
],
"metadata": {
"colab": {
@ -395,18 +395,18 @@
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CPU (Intel Xeon 2.00GHz)\n",
"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CPU (Intel Xeon 2.00GHz)\n",
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
"\n",
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from 'yolov8n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)\n",
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from 'yolo11n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)\n",
"\n",
"\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 2.2.1+cu121...\n",
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 2.0s, saved as 'yolov8n.torchscript' (12.4 MB)\n",
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 2.0s, saved as 'yolo11n.torchscript' (12.4 MB)\n",
"\n",
"Export complete (4.0s)\n",
"Results saved to \u001b[1m/content\u001b[0m\n",
"Predict: yolo predict task=detect model=yolov8n.torchscript imgsz=640 \n",
"Validate: yolo val task=detect model=yolov8n.torchscript imgsz=640 data=coco.yaml \n",
"Predict: yolo predict task=detect model=yolo11n.torchscript imgsz=640 \n",
"Validate: yolo val task=detect model=yolo11n.torchscript imgsz=640 data=coco.yaml \n",
"Visualize: https://netron.app\n",
"💡 Learn more at https://docs.ultralytics.com/modes/export\n"
]
@ -418,7 +418,7 @@
"source": [
"# 5. Python Usage\n",
"\n",
"YOLOv8 was reimagined using Python-first principles for the most seamless Python YOLO experience yet. YOLOv8 models can be loaded from a trained checkpoint or created from scratch. Then methods are used to train, val, predict, and export the model. See detailed Python usage examples in the [YOLOv8 Python Docs](https://docs.ultralytics.com/usage/python/)."
"YOLO11 was reimagined using Python-first principles for the most seamless Python YOLO experience yet. YOLO11 models can be loaded from a trained checkpoint or created from scratch. Then methods are used to train, val, predict, and export the model. See detailed Python usage examples in the [YOLO11 Python Docs](https://docs.ultralytics.com/usage/python/)."
],
"metadata": {
"id": "kUMOQ0OeDBJG"
@ -430,8 +430,8 @@
"from ultralytics import YOLO\n",
"\n",
"# Load a model\n",
"model = YOLO('yolov8n.yaml') # build a new model from scratch\n",
"model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)\n",
"model = YOLO('yolo11n.yaml') # build a new model from scratch\n",
"model = YOLO('yolo11n.pt') # load a pretrained model (recommended for training)\n",
"\n",
"# Use the model\n",
"results = model.train(data='coco8.yaml', epochs=3) # train the model\n",
@ -450,7 +450,7 @@
"source": [
"# 6. Tasks\n",
"\n",
"YOLOv8 can train, val, predict and export models for the most common tasks in vision AI: [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/). See [YOLOv8 Tasks Docs](https://docs.ultralytics.com/tasks/) for more information.\n",
"YOLO11 can train, val, predict and export models for the most common tasks in vision AI: [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/). See [YOLO11 Tasks Docs](https://docs.ultralytics.com/tasks/) for more information.\n",
"\n",
"<br><img width=\"1024\" src=\"https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png\">\n"
],
@ -463,7 +463,7 @@
"source": [
"## 1. Detection\n",
"\n",
"YOLOv8 _detection_ models have no suffix and are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on COCO. See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for full details.\n"
"YOLO11 _detection_ models have no suffix and are the default YOLO11 models, i.e. `yolo11n.pt` and are pretrained on COCO. See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for full details.\n"
],
"metadata": {
"id": "yq26lwpYK1lq"
@ -475,7 +475,7 @@
"# Load YOLOv8n, train it on COCO128 for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolov8n.pt') # load a pretrained YOLOv8n detection model\n",
"model = YOLO('yolo11n.pt') # load a pretrained YOLOv8n detection model\n",
"model.train(data='coco8.yaml', epochs=3) # train the model\n",
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
],
@ -490,7 +490,7 @@
"source": [
"## 2. Segmentation\n",
"\n",
"YOLOv8 _segmentation_ models use the `-seg` suffix, i.e. `yolov8n-seg.pt` and are pretrained on COCO. See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for full details.\n"
"YOLO11 _segmentation_ models use the `-seg` suffix, i.e. `yolo11n-seg.pt` and are pretrained on COCO. See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for full details.\n"
],
"metadata": {
"id": "7ZW58jUzK66B"
@ -502,7 +502,7 @@
"# Load YOLOv8n-seg, train it on COCO128-seg for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolov8n-seg.pt') # load a pretrained YOLOv8n segmentation model\n",
"model = YOLO('yolo11n-seg.pt') # load a pretrained YOLOv8n segmentation model\n",
"model.train(data='coco8-seg.yaml', epochs=3) # train the model\n",
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
],
@ -517,7 +517,7 @@
"source": [
"## 3. Classification\n",
"\n",
"YOLOv8 _classification_ models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on ImageNet. See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for full details.\n"
"YOLO11 _classification_ models use the `-cls` suffix, i.e. `yolo11n-cls.pt` and are pretrained on ImageNet. See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for full details.\n"
],
"metadata": {
"id": "ax3p94VNK9zR"
@ -529,7 +529,7 @@
"# Load YOLOv8n-cls, train it on mnist160 for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolov8n-cls.pt') # load a pretrained YOLOv8n classification model\n",
"model = YOLO('yolo11n-cls.pt') # load a pretrained YOLOv8n classification model\n",
"model.train(data='mnist160', epochs=3) # train the model\n",
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
],
@ -544,7 +544,7 @@
"source": [
"## 4. Pose\n",
"\n",
"YOLOv8 _pose_ models use the `-pose` suffix, i.e. `yolov8n-pose.pt` and are pretrained on COCO Keypoints. See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for full details."
"YOLO11 _pose_ models use the `-pose` suffix, i.e. `yolo11n-pose.pt` and are pretrained on COCO Keypoints. See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for full details."
],
"metadata": {
"id": "SpIaFLiO11TG"
@ -556,7 +556,7 @@
"# Load YOLOv8n-pose, train it on COCO8-pose for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolov8n-pose.pt') # load a pretrained YOLOv8n pose model\n",
"model = YOLO('yolo11n-pose.pt') # load a pretrained YOLOv8n pose model\n",
"model.train(data='coco8-pose.yaml', epochs=3) # train the model\n",
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
],
@ -571,7 +571,7 @@
"source": [
"## 4. Oriented Bounding Boxes (OBB)\n",
"\n",
"YOLOv8 _OBB_ models use the `-obb` suffix, i.e. `yolov8n-obb.pt` and are pretrained on the DOTA dataset. See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for full details."
"YOLO11 _OBB_ models use the `-obb` suffix, i.e. `yolo11n-obb.pt` and are pretrained on the DOTA dataset. See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for full details."
],
"metadata": {
"id": "cf5j_T9-B5F0"
@ -583,7 +583,7 @@
"# Load YOLOv8n-obb, train it on DOTA8 for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolov8n-obb.pt') # load a pretrained YOLOv8n OBB model\n",
"model = YOLO('yolo11n-obb.pt') # load a pretrained YOLOv8n OBB model\n",
"model.train(data='coco8-dota.yaml', epochs=3) # train the model\n",
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
],
@ -646,7 +646,7 @@
"source": [
"# Validate multiple models\n",
"for x in 'nsmlx':\n",
" !yolo val model=yolov8{x}.pt data=coco.yaml"
" !yolo val model=yolo11{x}.pt data=coco.yaml"
],
"metadata": {
"id": "Wdc6t_bfzDDk"

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