Update YOLO11 Actions and Docs (#16596)
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---
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comments: true
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description: Transform complex data into insightful heatmaps using Ultralytics YOLOv8. Discover patterns, trends, and anomalies with vibrant visualizations.
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keywords: Ultralytics, YOLOv8, heatmaps, data visualization, data analysis, complex data, patterns, trends, anomalies
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description: Transform complex data into insightful heatmaps using Ultralytics YOLO11. Discover patterns, trends, and anomalies with vibrant visualizations.
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keywords: Ultralytics, YOLO11, heatmaps, data visualization, data analysis, complex data, patterns, trends, anomalies
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---
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# Advanced [Data Visualization](https://www.ultralytics.com/glossary/data-visualization): Heatmaps using Ultralytics YOLOv8 🚀
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# Advanced [Data Visualization](https://www.ultralytics.com/glossary/data-visualization): Heatmaps using Ultralytics YOLO11 🚀
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## Introduction to Heatmaps
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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.
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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.
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<p align="center">
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<br>
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@ -18,7 +18,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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allowfullscreen>
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</iframe>
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<br>
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<strong>Watch:</strong> Heatmaps using Ultralytics YOLOv8
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<strong>Watch:</strong> Heatmaps using Ultralytics YOLO11
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</p>
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## Why Choose Heatmaps for Data Analysis?
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@ -31,15 +31,15 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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| Transportation | Retail |
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| :--------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------: |
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|  |  |
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| Ultralytics YOLOv8 Transportation Heatmap | Ultralytics YOLOv8 Retail Heatmap |
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|  |  |
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| Ultralytics YOLO11 Transportation Heatmap | Ultralytics YOLO11 Retail Heatmap |
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!!! tip "Heatmap Configuration"
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- `heatmap_alpha`: Ensure this value is within the range (0.0 - 1.0).
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- `decay_factor`: Used for removing heatmap after an object is no longer in the frame, its value should also be in the range (0.0 - 1.0).
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!!! example "Heatmaps using Ultralytics YOLOv8 Example"
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!!! example "Heatmaps using Ultralytics YOLO11 Example"
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=== "Heatmap"
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@ -48,7 +48,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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from ultralytics import YOLO, solutions
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model = YOLO("yolov8n.pt")
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model = YOLO("yolo11n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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assert cap.isOpened(), "Error reading video file"
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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@ -86,7 +86,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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from ultralytics import YOLO, solutions
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model = YOLO("yolov8n.pt")
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model = YOLO("yolo11n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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assert cap.isOpened(), "Error reading video file"
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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@ -127,7 +127,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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from ultralytics import YOLO, solutions
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model = YOLO("yolov8n.pt")
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model = YOLO("yolo11n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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assert cap.isOpened(), "Error reading video file"
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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@ -169,7 +169,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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from ultralytics import YOLO, solutions
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model = YOLO("yolov8n.pt")
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model = YOLO("yolo11n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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assert cap.isOpened(), "Error reading video file"
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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@ -211,7 +211,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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from ultralytics import YOLO, solutions
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model = YOLO("yolov8s.pt") # YOLOv8 custom/pretrained model
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model = YOLO("yolo11n.pt") # YOLO11 custom/pretrained model
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im0 = cv2.imread("path/to/image.png") # path to image file
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h, w = im0.shape[:2] # image height and width
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@ -236,7 +236,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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from ultralytics import YOLO, solutions
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model = YOLO("yolov8n.pt")
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model = YOLO("yolo11n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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assert cap.isOpened(), "Error reading video file"
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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@ -326,20 +326,20 @@ These colormaps are commonly used for visualizing data with different color repr
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## FAQ
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### How does Ultralytics YOLOv8 generate heatmaps and what are their benefits?
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### How does Ultralytics YOLO11 generate heatmaps and what are their benefits?
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Ultralytics YOLOv8 generates heatmaps by transforming complex data into a color-coded matrix where different hues represent data intensities. Heatmaps make it easier to visualize patterns, correlations, and anomalies in the data. Warmer hues indicate higher values, while cooler tones represent lower values. The primary benefits include intuitive visualization of data distribution, efficient pattern detection, and enhanced spatial analysis for decision-making. For more details and configuration options, refer to the [Heatmap Configuration](#arguments-heatmap) section.
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Ultralytics YOLO11 generates heatmaps by transforming complex data into a color-coded matrix where different hues represent data intensities. Heatmaps make it easier to visualize patterns, correlations, and anomalies in the data. Warmer hues indicate higher values, while cooler tones represent lower values. The primary benefits include intuitive visualization of data distribution, efficient pattern detection, and enhanced spatial analysis for decision-making. For more details and configuration options, refer to the [Heatmap Configuration](#arguments-heatmap) section.
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### Can I use Ultralytics YOLOv8 to perform object tracking and generate a heatmap simultaneously?
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### Can I use Ultralytics YOLO11 to perform object tracking and generate a heatmap simultaneously?
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Yes, Ultralytics YOLOv8 supports object tracking and heatmap generation concurrently. This can be achieved through its `Heatmap` solution integrated with object tracking models. To do so, you need to initialize the heatmap object and use YOLOv8's tracking capabilities. Here's a simple example:
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Yes, Ultralytics YOLO11 supports object tracking and heatmap generation concurrently. This can be achieved through its `Heatmap` solution integrated with object tracking models. To do so, you need to initialize the heatmap object and use YOLO11's tracking capabilities. Here's a simple example:
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```python
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import cv2
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from ultralytics import YOLO, solutions
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model = YOLO("yolov8n.pt")
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model = YOLO("yolo11n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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heatmap_obj = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, view_img=True, shape="circle", names=model.names)
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For further guidance, check the [Tracking Mode](../modes/track.md) page.
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### What makes Ultralytics YOLOv8 heatmaps different from other data visualization tools like those from [OpenCV](https://www.ultralytics.com/glossary/opencv) or Matplotlib?
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### What makes Ultralytics YOLO11 heatmaps different from other data visualization tools like those from [OpenCV](https://www.ultralytics.com/glossary/opencv) or Matplotlib?
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Ultralytics YOLOv8 heatmaps are specifically designed for integration with its [object detection](https://www.ultralytics.com/glossary/object-detection) and tracking models, providing an end-to-end solution for real-time data analysis. Unlike generic visualization tools like OpenCV or Matplotlib, YOLOv8 heatmaps are optimized for performance and automated processing, supporting features like persistent tracking, decay factor adjustment, and real-time video overlay. For more information on YOLOv8's unique features, visit the [Ultralytics YOLOv8 Introduction](https://www.ultralytics.com/blog/introducing-ultralytics-yolov8).
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Ultralytics YOLO11 heatmaps are specifically designed for integration with its [object detection](https://www.ultralytics.com/glossary/object-detection) and tracking models, providing an end-to-end solution for real-time data analysis. Unlike generic visualization tools like OpenCV or Matplotlib, YOLO11 heatmaps are optimized for performance and automated processing, supporting features like persistent tracking, decay factor adjustment, and real-time video overlay. For more information on YOLO11's unique features, visit the [Ultralytics YOLO11 Introduction](https://www.ultralytics.com/blog/introducing-ultralytics-yolov8).
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### How can I visualize only specific object classes in heatmaps using Ultralytics YOLOv8?
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### How can I visualize only specific object classes in heatmaps using Ultralytics YOLO11?
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You can visualize specific object classes by specifying the desired classes in the `track()` method of the YOLO model. For instance, if you only want to visualize cars and persons (assuming their class indices are 0 and 2), you can set the `classes` parameter accordingly.
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from ultralytics import YOLO, solutions
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model = YOLO("yolov8n.pt")
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model = YOLO("yolo11n.pt")
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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heatmap_obj = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, view_img=True, shape="circle", names=model.names)
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cv2.destroyAllWindows()
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```
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### Why should businesses choose Ultralytics YOLOv8 for heatmap generation in data analysis?
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### Why should businesses choose Ultralytics YOLO11 for heatmap generation in data analysis?
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Ultralytics YOLOv8 offers seamless integration of advanced object detection and real-time heatmap generation, making it an ideal choice for businesses looking to visualize data more effectively. The key advantages include intuitive data distribution visualization, efficient pattern detection, and enhanced spatial analysis for better decision-making. Additionally, YOLOv8's cutting-edge features such as persistent tracking, customizable colormaps, and support for various export formats make it superior to other tools like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and OpenCV for comprehensive data analysis. Learn more about business applications at [Ultralytics Plans](https://www.ultralytics.com/plans).
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Ultralytics YOLO11 offers seamless integration of advanced object detection and real-time heatmap generation, making it an ideal choice for businesses looking to visualize data more effectively. The key advantages include intuitive data distribution visualization, efficient pattern detection, and enhanced spatial analysis for better decision-making. Additionally, YOLO11's cutting-edge features such as persistent tracking, customizable colormaps, and support for various export formats make it superior to other tools like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and OpenCV for comprehensive data analysis. Learn more about business applications at [Ultralytics Plans](https://www.ultralytics.com/plans).
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