

YOLO11
Overview :
Ultralytics YOLO11 is further developed from the previous YOLO series models, introducing new features and improvements to enhance performance and flexibility. YOLO11 is designed to be fast, accurate, and easy to use, making it ideal for a wide range of tasks including object detection, tracking, instance segmentation, image classification, and pose estimation.
Target Users :
The target audience includes AI researchers, data scientists, machine learning engineers, and students who need a fast, accurate, and user-friendly model for image recognition and analysis.
Use Cases
Detecting vehicles and pedestrians in traffic monitoring systems.
Analyzing customer behavior in retail environments.
Identifying lesions in medical image analysis.
Features
Supports multiple tasks: object detection, tracking, instance segmentation, image classification, and pose estimation.
Provides pre-trained models: detection, segmentation, and pose models pre-trained on the COCO dataset, as well as classification models pre-trained on the ImageNet dataset.
Usable directly in both command line interface (CLI) and Python environments.
Supports model export to ONNX format.
Offers various model sizes and performance levels to suit different application scenarios.
Integrates with leading AI platforms such as Roboflow, ClearML, Comet, Neural Magic, and OpenVINO to optimize AI workflows.
Provides Ultralytics HUB, an integrated solution for data visualization, model training, and deployment without requiring coding.
How to Use
Install the ultralytics package via pip, which includes all dependencies.
Load the pre-trained model using the CLI or Python environment.
Train or evaluate the model as needed.
Use the model for object detection or other tasks on images.
If necessary, export the model to ONNX format.
Utilize Ultralytics HUB for model training and deployment.
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