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Update YOLO11 Actions and Docs (#16596)

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docs/en/guides/object-counting.md
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@ -1,14 +1,14 @@
---
comments: true
description: Learn to accurately identify and count objects in real-time using Ultralytics YOLOv8 for applications like crowd analysis and surveillance.
keywords: object counting, YOLOv8, Ultralytics, real-time object detection, AI, deep learning, object tracking, crowd analysis, surveillance, resource optimization
description: Learn to accurately identify and count objects in real-time using Ultralytics YOLO11 for applications like crowd analysis and surveillance.
keywords: object counting, YOLO11, Ultralytics, real-time object detection, AI, deep learning, object tracking, crowd analysis, surveillance, resource optimization
---
# Object Counting using Ultralytics YOLOv8
# Object Counting using Ultralytics YOLO11
## What is Object Counting?
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](https://www.ultralytics.com/glossary/deep-learning-dl) capabilities.
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](https://www.ultralytics.com/glossary/deep-learning-dl) capabilities.
<table>
<tr>
@ -19,7 +19,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Object Counting using Ultralytics YOLOv8
<strong>Watch:</strong> Object Counting using Ultralytics YOLO11
</td>
<td align="center">
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/Fj9TStNBVoY"
@ -28,7 +28,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Class-wise Object Counting using Ultralytics YOLOv8
<strong>Watch:</strong> Class-wise Object Counting using Ultralytics YOLO11
</td>
</tr>
</table>
@ -43,10 +43,10 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
| Logistics | Aquaculture |
| :-----------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------: |
| ![Conveyor Belt Packets Counting Using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/conveyor-belt-packets-counting.avif) | ![Fish Counting in Sea using Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/fish-counting-in-sea-using-ultralytics-yolov8.avif) |
| Conveyor Belt Packets Counting Using Ultralytics YOLOv8 | Fish Counting in Sea using Ultralytics YOLOv8 |
| ![Conveyor Belt Packets Counting Using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/conveyor-belt-packets-counting.avif) | ![Fish Counting in Sea using Ultralytics YOLO11](https://github.com/ultralytics/docs/releases/download/0/fish-counting-in-sea-using-ultralytics-yolov8.avif) |
| Conveyor Belt Packets Counting Using Ultralytics YOLO11 | Fish Counting in Sea using Ultralytics YOLO11 |
!!! example "Object Counting using YOLOv8 Example"
!!! example "Object Counting using YOLO11 Example"
=== "Count in Region"
@ -55,7 +55,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
from ultralytics import YOLO, solutions
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@ -97,7 +97,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
from ultralytics import YOLO, solutions
model = YOLO("yolov8n-obb.pt")
model = YOLO("yolo11n-obb.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@ -137,7 +137,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
from ultralytics import YOLO, solutions
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@ -178,7 +178,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
from ultralytics import YOLO, solutions
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@ -219,7 +219,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
from ultralytics import YOLO, solutions
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@ -277,12 +277,12 @@ Here's a table with the `ObjectCounter` arguments:
## FAQ
### How do I count objects in a video using Ultralytics YOLOv8?
### How do I count objects in a video using Ultralytics YOLO11?
To count objects in a video using Ultralytics YOLOv8, you can follow these steps:
To count objects in a video using Ultralytics YOLO11, you can follow these steps:
1. Import the necessary libraries (`cv2`, `ultralytics`).
2. Load a pretrained YOLOv8 model.
2. Load a pretrained YOLO11 model.
3. Define the counting region (e.g., a polygon, line, etc.).
4. Set up the video capture and initialize the object counter.
5. Process each frame to track objects and count them within the defined region.
@ -321,14 +321,14 @@ def count_objects_in_region(video_path, output_video_path, model_path):
cv2.destroyAllWindows()
count_objects_in_region("path/to/video.mp4", "output_video.avi", "yolov8n.pt")
count_objects_in_region("path/to/video.mp4", "output_video.avi", "yolo11n.pt")
```
Explore more configurations and options in the [Object Counting](#object-counting-using-ultralytics-yolov8) section.
Explore more configurations and options in the [Object Counting](#object-counting-using-ultralytics-yolo11) section.
### What are the advantages of using Ultralytics YOLOv8 for object counting?
### What are the advantages of using Ultralytics YOLO11 for object counting?
Using Ultralytics YOLOv8 for object counting offers several advantages:
Using Ultralytics YOLO11 for object counting offers several advantages:
1. **Resource Optimization:** It facilitates efficient resource management by providing accurate counts, helping optimize resource allocation in industries like inventory management.
2. **Enhanced Security:** It enhances security and surveillance by accurately tracking and counting entities, aiding in proactive threat detection.
@ -336,9 +336,9 @@ Using Ultralytics YOLOv8 for object counting offers several advantages:
For real-world applications and code examples, visit the [Advantages of Object Counting](#advantages-of-object-counting) section.
### How can I count specific classes of objects using Ultralytics YOLOv8?
### How can I count specific classes of objects using Ultralytics YOLO11?
To count specific classes of objects using Ultralytics YOLOv8, you need to specify the classes you are interested in during the tracking phase. Below is a Python example:
To count specific classes of objects using Ultralytics YOLO11, you need to specify the classes you are interested in during the tracking phase. Below is a Python example:
```python
import cv2
@ -372,27 +372,27 @@ def count_specific_classes(video_path, output_video_path, model_path, classes_to
cv2.destroyAllWindows()
count_specific_classes("path/to/video.mp4", "output_specific_classes.avi", "yolov8n.pt", [0, 2])
count_specific_classes("path/to/video.mp4", "output_specific_classes.avi", "yolo11n.pt", [0, 2])
```
In this example, `classes_to_count=[0, 2]`, which means it counts objects of class `0` and `2` (e.g., person and car).
### Why should I use YOLOv8 over other [object detection](https://www.ultralytics.com/glossary/object-detection) models for real-time applications?
### Why should I use YOLO11 over other [object detection](https://www.ultralytics.com/glossary/object-detection) models for real-time applications?
Ultralytics YOLOv8 provides several advantages over other object detection models like Faster R-CNN, SSD, and previous YOLO versions:
Ultralytics YOLO11 provides several advantages over other object detection models like Faster R-CNN, SSD, and previous YOLO versions:
1. **Speed and Efficiency:** YOLOv8 offers real-time processing capabilities, making it ideal for applications requiring high-speed inference, such as surveillance and autonomous driving.
1. **Speed and Efficiency:** YOLO11 offers real-time processing capabilities, making it ideal for applications requiring high-speed inference, such as surveillance and autonomous driving.
2. **[Accuracy](https://www.ultralytics.com/glossary/accuracy):** It provides state-of-the-art accuracy for object detection and tracking tasks, reducing the number of false positives and improving overall system reliability.
3. **Ease of Integration:** YOLOv8 offers seamless integration with various platforms and devices, including mobile and edge devices, which is crucial for modern AI applications.
3. **Ease of Integration:** YOLO11 offers seamless integration with various platforms and devices, including mobile and edge devices, which is crucial for modern AI applications.
4. **Flexibility:** Supports various tasks like object detection, segmentation, and tracking with configurable models to meet specific use-case requirements.
Check out Ultralytics [YOLOv8 Documentation](https://docs.ultralytics.com/models/yolov8/) for a deeper dive into its features and performance comparisons.
Check out Ultralytics [YOLO11 Documentation](https://docs.ultralytics.com/models/yolo11/) for a deeper dive into its features and performance comparisons.
### Can I use YOLOv8 for advanced applications like crowd analysis and traffic management?
### Can I use YOLO11 for advanced applications like crowd analysis and traffic management?
Yes, Ultralytics YOLOv8 is perfectly suited for advanced applications like crowd analysis and traffic management due to its real-time detection capabilities, scalability, and integration flexibility. Its advanced features allow for high-accuracy object tracking, counting, and classification in dynamic environments. Example use cases include:
Yes, Ultralytics YOLO11 is perfectly suited for advanced applications like crowd analysis and traffic management due to its real-time detection capabilities, scalability, and integration flexibility. Its advanced features allow for high-accuracy object tracking, counting, and classification in dynamic environments. Example use cases include:
- **Crowd Analysis:** Monitor and manage large gatherings, ensuring safety and optimizing crowd flow.
- **Traffic Management:** Track and count vehicles, analyze traffic patterns, and manage congestion in real-time.
For more information and implementation details, refer to the guide on [Real World Applications](#real-world-applications) of object counting with YOLOv8.
For more information and implementation details, refer to the guide on [Real World Applications](#real-world-applications) of object counting with YOLO11.