Ultralytics introduced an new model to the YOLO family: YOLOv11. YOLOv11 aims to improve performance benchmarks in object detection.
In this article, we explore its key features, provide guidance on running inference. YOLOv11 is engineered for speed, precision, and versatility, making it a best solution for tasks like object detection, segmentation, classification, pose estimation, and tracking.
New Stuff in YOLOv11
The YOLOv11 model sets a new benchmark in computer vision with its advanced architecture and optimization. It is designed to provide faster inference times while maintaining exceptional accuracy.
In comparison to YOLOv10, YOLOv11 have impressive increase in mean average precision (mAP) on the COCO dataset, coupled with better Enhanced Feature Extraction.
The diagram highlight YOLOv11 as a clear winner, surpassing earlier models in both accuracy and inference speed, as demonstrated on the COCO dataset.
Supported Methods in YOLOv11
YOLOv11 supports a broad range of computer vision tasks, like:
Object Detection: Locating objects in images or videos with bounding boxes and confidence scores. Crucial for applications like automated retail checkouts, traffic monitoring, and smart surveillance.
Instance Segmentation: Differentiating individual objects within an image. Use cases like precision agriculture, medical imaging, and inventory management.
Pose Estimation: Identifying human body key points for gesture recognition, sports analysis, or physical rehabilitation.
Oriented Object Detection: Detecting and localizing rotated or tilted objects. Its particularly valuable for drone imaging, satellite mapping, and industrial inspections.
Many more are there, checkout the main release doc.
Tryout YOLOv11 Get your Hands Dirty
To use YOLOv11, lets spin up a new colab notebook.
Install or update the Ultralytics package:
!pip install ultralytics --upgrade
Run inference on an image:
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Define path to the image file
source = "/content/run-or-walk-1-1.jpg"
# Run inference on the source
results = model(source) # list of Results objects
results[0].show()
Advantages of YOLOv11
Lightweight Yet Powerful: With fewer parameters than YOLOv10, YOLOv11 achieves superior performance.
Faster Inference: Ideal for real-time applications, processing an image in under 5ms on high-end GPUs.(works really fast with colab also)
Seamless integration across edge devices, cloud platforms, and custom deployments.
Conclusion
YOLOv11 is a remarkable leap forward in the YOLO series, offering faster processing speeds, improved accuracy, and multi-tasking features. Whether you’re a researcher, developer, or business professional, YOLOv11 provides a versatile solution for various computer vision challenges. Hope you are impressed with YOLOv11.
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