Ultralytics-YOLOv8-detection-Ara240

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Overview

Ultralytics released Ultralytics YOLOv8 on January 10, 2023, delivering state‑of‑the‑art accuracy and speed. Building on the progress of earlier Ultralytics YOLO versions, it introduces improved features and optimizations that make it a strong choice for a wide range of object detection, segmentation and pose estimation tasks across many applications.

Key Features of Ultralytics YOLOv8

• Advanced Backbone and Neck Architectures: YOLOv8 incorporates modern backbone and neck designs that enhance feature extraction and overall detection performance.
• Anchor‑Free Split Ultralytics Head: By using an anchor‑free split head, YOLOv8 achieves higher accuracy and more efficient detection compared to traditional anchor‑based methods.
• Balanced Accuracy–Speed Optimization: YOLOv8 is engineered to deliver strong accuracy while maintaining real‑time speed, making it well‑suited for a wide range of real‑time detection applications.
• Multiple Pretrained Model Options: A variety of pretrained models are available, allowing users to choose the one that best matches their task requirements and performance needs.

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Model Description

This is a repository that contains a set of quantized and compiled versions of Ultralytics YOLOv8 detection models optimized for Ara240 DNPU. These models predict bounding boxes to locate objects on an image.

• Base Model: Ultralytics/YOLOv8
• Original Model Authors: Ultralytics
• Original License: AGPL-3.0

Modifications

These pre-compiled models are based on the original Ultralytics YOLOv8 models, with below modifications:

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

For more information on quantization and compilation procedures, refer to the Ara-Model-Zoo. There you will find the steps and recipes used to compile and quantize the models for Ara240 DNPU deployment.

Getting the Models

This repository uses git tags to version the pre-compiled DVM files by Ara SDK release. Use the Hugging Face CLI to download a specific model for a given SDK release:

# Download a single model for a specific Ara SDK release (e.g., r3.0)
hf download nxp/Ultralytics-YOLOv8-detection-Ara240 yolov8n.dvm --revision r3.0 --local-dir .

# Download all models for a specific release
hf download nxp/Ultralytics-YOLOv8-detection-Ara240 --revision r3.0 --local-dir .

The --local-dir . flag saves the files directly to the current directory instead of the default Hugging Face cache (~/.cache/huggingface/hub).

Replace yolov8n.dvm with yolov8s.dvm, yolov8m.dvm, yolov8l.dvm, or yolov8x.dvm for other model variants.

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.

AGPL-3.0 Source Code Availability

As required by the AGPL-3.0 license, the complete corresponding source code for the software used to produce these pre-compiled models is made available as follows:

• Model source code: All software components and their respective source locations are listed in the SBOM.spdx.json file included in this repository.
• Compilation and quantization recipes: The full steps and scripts used to quantize and compile the models into DVM format are available at Ara-Model-Zoo.

Note: The NXP Ara SDK used to produce the DVM files is a proprietary NXP tool and is not subject to the AGPL-3.0 license. Under AGPL-3.0 Section 1, general-purpose compilation tools used are explicitly excluded from the "Corresponding Source" requirement.