--- license: agpl-3.0 pipeline_tag: object-detection base_model: - Ultralytics/YOLOv8 tags: - ultralytics - object-detection - yolov8 - quantized - int8 ---

YOLO Vision banner

License AGPL-3.0 Static Badge
# 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.

YOLOv8 performance comparison charts

## 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
(pixels) | FP32 mAPval
50-95 | INT8 mAPval
50-95 | Latency
Ara240
(ms) | Performance
Ara240
(inferences/s) | params
(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 | - **mAPval** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. ### Segmentation | Model | size
(pixels) | FP32 mAPmask
50-95 | INT8 mAPmask
50-95 | Latency
Ara240
(ms) | Performance
Ara240
(inferences/s) | params
(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 | - **mAPmask** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. ### Pose | Model | size
(pixels) | FP32 mAPpose
50-95 | INT8 mAPpose
50-95 | Latency
Ara240
(ms) | Performance
Ara240
(inferences/s) | params
(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 | - **mAPpose** 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).