nxp/Ultralytics-YOLO11-Detection-Ara240

Overview

Ultralytics released Ultralytics YOLO11 on September 10, 2024, delivering state-of-the-art accuracy and speed. Building on the progress of earlier Ultralytics YOLO versions, YOLO11 introduces improved features and optimizations that make it a strong choice for a wide range of object detection, instance segmentation, classificationand pose estimation tasks across many applications.

Key Features of Ultralytics YOLO11

• Advanced Backbone and Neck Architectures: YOLO11 enhances the backbone and neck components of previous versions, improving feature extraction and boosting performance across different tasks.
• Efficient Performance Balance: The architecture used across the different YOLO11 models achieves an optimal trade-off between model size and accuracy, enabling short inference times without compromising performance.
• Higher Accuracy with Fewer Parameters: As per YOLO11 architecture, YOLO11m has almost 22% fewer parameters than YOLO8m, while still achieving a higher Mean Average Precision (mAP).
• Multi-Environment Deployment: YOLO11's versatility enables fast deployment in cloud platforms, edge devices, and systems supporting NVIDIA GPUs.
• Multi-Task Capabilities: YOLO11 supports traditional object detection and image classification, as well as pose estimation and oriented bounding box detection (OBB). Note: The models included in this repository are for object detection only.

Model Description

This repository contains Ultralytics YOLO11 pre-compiled models for object detection optimized for NXP Ara240 DNPU.

• Base Model: Ultralytics/YOLO11
• Original Model Authors: Ultralytics
• Original License: AGPL-3.0
• Modified by: NXP

Modifications

This model is a derivative work with the following changes from the original:

• Quantization: INT8, calibrated using COCO val2017
• Compilation: Compiled for the Ara240 DNPU
• Format: Converted to DVM format for NPU deployment

The original model is available at: Ultralytics YOLO11.

Performance Summary

Object Detection

Model	Size (pixels)	FP32 mAPval 50-95	INT8 mAPval 50-95	Latency Ara240 (ms)	Performance Ara240 (inferences/s)	Params (M)
YOLO11n	640	39.5	35.29	3.14	317.86	2.6
YOLO11s	640	47.0	45.02	6.71	148.83	9.4
YOLO11m	640	51.5	49.64	20.82	48.01	20.1
YOLO11l	640	53.4	51.25	28.16	35.50	25.3
YOLO11x	640	54.7	51.87	59.17	16.89	56.9

• mAP^val values are measured on the COCO val2017 validation dataset.

License

Ultralytics offers two licensing options to accommodate diverse use cases:

• AGPL-3.0 License: This OSI-approved open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the LICENSE file for more details.
• Enterprise License: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through Ultralytics Licensing.