`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8 [CLI Docs](https://docs.ultralytics.com/usage/cli) for examples.
`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8 [CLI Docs](https://docs.ultralytics.com/usage/cli) for examples.
#### Python
### Python
YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
@ -98,6 +98,18 @@ See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more exa
</details>
</details>
### Notebooks
Ultralytics provides interactive notebooks for YOLOv8, covering training, validation, tracking, and more. Each notebook is paired with a [YouTube](https://youtube.com/ultralytics) tutorial, making it easy to learn and implement advanced YOLOv8 features.
| <ahref="https://docs.ultralytics.com/modes/track/">YOLOv8 Multi-Object Tracking in Videos</a> | <ahref="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_tracking.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a> | <ahref="https://youtu.be/hHyHmOtmEgs"><center><imgwidth=30%src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png"alt="Ultralytics Youtube Video"></center></a> |
| <ahref="https://docs.ultralytics.com/guides/object-counting/">YOLOv8 Object Counting in Videos</a> | <ahref="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_counting.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a> | <ahref="https://youtu.be/Ag2e-5_NpS0"><center><imgwidth=30%src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png"alt="Ultralytics Youtube Video"></center></a> |
| <ahref="https://docs.ultralytics.com/guides/heatmaps/">YOLOv8 Heatmaps in Videos</a> | <ahref="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/heatmaps.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a> | <ahref="https://youtu.be/4ezde5-nZZw"><center><imgwidth=30%src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png"alt="Ultralytics Youtube Video"></center></a> |
## <divalign="center">Models</div>
## <divalign="center">Models</div>
YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) dataset. [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models.
YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) dataset. [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models.
" <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/heatmaps.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></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 <a href=\"https://docs.ultralytics.com/guides/heatmaps/\">heatmaps</a> 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",
"\n",
"We hope that the resources in this notebook will help you get the most out of <a href=\"https://docs.ultralytics.com/guides/heatmaps/\">Ultralytics Heatmaps</a>. 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",
"\n",
"</div>"
],
"metadata": {
"id": "PN1cAxdvd61e"
}
},
{
"cell_type": "markdown",
"source": [
"# Setup\n",
"\n",
"Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) and check software and hardware."
],
"metadata": {
"id": "o68Sg1oOeZm2"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9dSwz_uOReMI"
},
"outputs": [],
"source": [
"!pip install ultralytics"
]
},
{
"cell_type": "markdown",
"source": [
"# Ultralytics Heatmaps\n",
"\n",
"Heatmap is color-coded matrix, generated by Ultralytics YOLOv8, simplifies intricate data by using vibrant colors. This visual representation employs warmer hues for higher intensities and cooler tones for lower values. Heatmaps are effective in illustrating complex data patterns, correlations, and anomalies, providing a user-friendly and engaging way to interpret data across various domains."
" <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_counting.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></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 <a href=\"https://docs.ultralytics.com/guides/object-counting/\">Object Counting</a> 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",
"\n",
"We hope that the resources in this notebook will help you get the most out of <a href=\"https://docs.ultralytics.com/guides/object-counting/\">Ultralytics Object Counting</a>. 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",
"\n",
"</div>"
],
"metadata": {
"id": "PN1cAxdvd61e"
}
},
{
"cell_type": "markdown",
"source": [
"# Setup\n",
"\n",
"Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) and check software and hardware."
],
"metadata": {
"id": "o68Sg1oOeZm2"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9dSwz_uOReMI"
},
"outputs": [],
"source": [
"!pip install ultralytics"
]
},
{
"cell_type": "markdown",
"source": [
"# Ultralytics Object Counting\n",
"\n",
"Counting objects using Ultralytics YOLOv8 entails the precise detection and enumeration of specific objects within videos and camera streams. YOLOv8 demonstrates exceptional performance in real-time applications, delivering efficient and accurate object counting across diverse scenarios such as crowd analysis and surveillance. This is attributed to its advanced algorithms and deep learning capabilities."
" <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_tracking.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></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 <a href=\"https://docs.ultralytics.com/modes/track/\">Object Tracking</a> 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",
"\n",
"We hope that the resources in this notebook will help you get the most out of <a href=\"https://docs.ultralytics.com/modes/track/\">Ultralytics Object Tracking</a>. 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",
"\n",
"</div>"
],
"metadata": {
"id": "PN1cAxdvd61e"
}
},
{
"cell_type": "markdown",
"source": [
"# Setup\n",
"\n",
"Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) and check software and hardware."
],
"metadata": {
"id": "o68Sg1oOeZm2"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9dSwz_uOReMI"
},
"outputs": [],
"source": [
"!pip install ultralytics"
]
},
{
"cell_type": "markdown",
"source": [
"# Ultralytics Object Tracking\n",
"\n",
"Within the domain of video analytics, object tracking stands out as a crucial undertaking. It goes beyond merely identifying the location and class of objects within the frame; it also involves assigning a unique ID to each detected object as the video unfolds. The applications of this technology are vast, spanning from surveillance and security to real-time sports analytics."
Glenn Jocher
11 months ago
committed by
GitHub