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license: agpl-3.0
pipeline_tag: object-detection
base_model:
- Ultralytics/YOLOv8
tags:
- ultralytics
- object-detection
- yolov8
- quantized
- int8
---
<div align="center">
<p>
<a href="https://www.ultralytics.com/events/yolovision/?utm_source=nxp&utm_medium=referral&utm_campaign=partner" target="_blank">
<img width="100%" src="https://cdn.prod.website-files.com/680a070c3b99253410dd3dcf/68e4ed994ba1820276d637b8_Ultralytics%20YOLOv8.svg" alt="YOLO Vision banner"></a>
</p>
<div>
<a href="https://huggingface.co/nxp/YOLOv8/blob/main/LICENSE.txt"><img src="https://img.shields.io/badge/License-AGPL%203.0-blue" alt="License AGPL-3.0"></a>
<a href="https://www.ultralytics.com/"><img alt="Static Badge" src="https://img.shields.io/badge/Ultralytics-blue?logo=ultralytics&label=Origin&link=https%3A%2F%2Fwww.ultralytics.com%2F"></a>
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</div>
# Overview
[Ultralytics](https://www.ultralytics.com/?utm_source=nxp&utm_medium=referral&utm_campaign=partner) released [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/?utm_source=nxp&utm_medium=referral&utm_campaign=partner) 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.
<div align="center">
<p>
<a href="https://docs.ultralytics.com/models/yolov8/?utm_source=nxp&utm_medium=referral&utm_campaign=partner" target="_blank">
<img width="100%" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/yolov8-comparison-plots.avif" alt="YOLOv8 performance comparison charts"></a>
</p>
</div>
## Model Description
This is a repository that contains a set of quantized and compiled versions of [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/) models optimized for Ara240 DNPU.
- **Base Model:** [Ultralytics/YOLOv8](https://github.com/ultralytics/ultralytics)
- **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](https://cocodataset.org/)
- **Compilation:** Compiled for Ara240 DNPU
- **Format:** Converted to DVM format for NPU deployment
Original model available at: [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/).
## Performance Summary
### Object Detection
| Model | size<br><sup>(pixels) | FP32 mAP<sup>val<br>50-95 | INT8 mAP<sup>val<br>50-95 | Latency <br><sup>Ara240<br>(ms) | Performance<br><sup>Ara240<br>(inferences/s) | params<br><sup>(M) |
| ------- | --------------------- | ------------------------- | ------------------------- | ------------------------------- | -------------------------------------------- | ------------------ |
| YOLOv8n | 640 | 37.3 | 35.5 | 3.15 | 316.99 | 3.2 |
| YOLOv8s | 640 | 44.9 | 43.4 | 6.72 | 148.72 | 11.2 |
| YOLOv8m | 640 | 50.2 | 48.7 | 17.60 | 56.80 | 25.9 |
| YOLOv8l | 640 | 52.9 | 51.4 | 35.96 | 27.80 | 43.7 |
| YOLOv8x | 640 | 53.9 | 49.7 | 56.02 | 17.84 | 68.2 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset.
### Segmentation
| Model | size<br><sup>(pixels) | FP32 mAP<sup>mask<br>50-95 | INT8 mAP<sup>mask<br>50-95 | Latency <br><sup>Ara240<br>(ms) | Performance<br><sup>Ara240<br>(inferences/s) | params<br><sup>(M) |
| ----------- | --------------------- | -------------------------- | -------------------------- | ------------------------------- | -------------------------------------------- | ------------------ |
| YOLOv8n-seg | 640 | 30.5 | 29.18 | 4.13 | 241.88 | 3.4 |
| YOLOv8s-seg | 640 | 36.8 | 36.10 | 10.14 | 98.61 | 11.8 |
| YOLOv8m-seg | 640 | 40.8 | 39.71 | 25.07 | 39.88 | 27.3 |
| YOLOv8l-seg | 640 | 42.6 | 39.15 | 49.87 | 20.04 | 46 |
| YOLOv8x-seg | 640 | 43.4 | 39.6 | 78.91 | 12.67 | 71.8 |
- **mAP<sup>mask</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset.
### Pose
| Model | size<br><sup>(pixels) | FP32 mAP<sup>pose<br>50-95 | INT8 mAP<sup>pose<br>50-95 | Latency <br><sup>Ara240<br>(ms) | Performance<br><sup>Ara240<br>(inferences/s) | params<br><sup>(M) |
| ------------ | --------------------- | -------------------------- | -------------------------- | ------------------------------- | -------------------------------------------- | ------------------ |
| YOLOv8n-pose | 640 | 50.4 | 46.75 | 4.08 | 245.01 | 3.3 |
| YOLOv8s-pose | 640 | 60 | 53.66 | 8.07 | 123.88 | 11.6 |
| YOLOv8m-pose | 640 | 65 | 58.84 | 19.02 | 52.56 | 26.4 |
| YOLOv8l-pose | 640 | 67.6 | 62.84 | 36.85 | 27.13 | 44.4 |
| YOLOv8x-pose | 640 | 69.2 | 64.76 | 59.48 | 16.8 | 69.4 |
- **mAP<sup>pose</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset.
## License
Ultralytics offers two licensing options to accommodate diverse use cases:
- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://www.ultralytics.com/license/?utm_source=nxp&utm_medium=referral&utm_campaign=partner) 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](https://www.ultralytics.com/license/?utm_source=nxp&utm_medium=referral&utm_campaign=partner). |