> For the complete documentation index, see [llms.txt](https://docs.degirum.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.degirum.com/ai-hub/public-models/axelera-ai.md).

# Axelera AI

Models are located at the [Axelera AI model zoo on the AI Hub](https://hub.degirum.com/public-models/degirum/axelera?utm_source=docs.degirum.com\&utm_medium=site\&utm_campaign=ai-hub-public-models-axelera-ai).

## Models by DeGirum

### Axelera Metis

**Detection**

| Model Name                                                         | Use Case                                      | mAP 50‑95                              | mAP 50                                 |
| ------------------------------------------------------------------ | --------------------------------------------- | -------------------------------------- | -------------------------------------- |
| yolov8n\_coco--640x640\_quant\_axelera\_metis\_1                   | COCO Detection                                | 0.3561                                 | 0.5059                                 |
| yolov8n\_coco\_seg--640x640\_quant\_axelera\_metis\_1              | COCO Instance Segmentation                    | <p>bbox: 0.3510</p><p>mask: 0.2940</p> | <p>bbox: 0.5015</p><p>mask: 0.4706</p> |
| yolov8l\_coco--640x640\_quant\_axelera\_metis\_1                   | COCO Detection                                | 0.5140                                 | 0.6853                                 |
| yolov8m\_coco--640x640\_quant\_axelera\_metis\_1                   | COCO Detection                                | 0.4825                                 | 0.6531                                 |
| yolov8s\_coco--640x640\_quant\_axelera\_metis\_1                   | COCO Detection                                | 0.4375                                 | 0.6053                                 |
| yolov9t\_coco--640x640\_quant\_axelera\_metis\_1                   | COCO Detection                                | 0.3665                                 | 0.5119                                 |
| yolov8n\_coco\_pose--640x640\_quant\_axelera\_metis\_1             | COCO Pose Keypoints                           | <p>bbox: 0.5160</p><p>kpts: 0.4838</p> | <p>bbox: 0.7059</p><p>kpts: 0.7894</p> |
| yolov8n\_relu6\_face--640x640\_quant\_axelera\_metis\_1            | Face Detection                                | 0.5645                                 | 0.7834                                 |
| yolov8n\_relu6\_hand--640x640\_quant\_axelera\_metis\_1            | Hand Detection                                | 0.4453                                 | 0.7550                                 |
| yolov8n\_relu6\_person--640x640\_quant\_axelera\_metis\_1          | Person Detection                              | 0.3119                                 | 0.4993                                 |
| yolov8n\_relu6\_ppe--640x640\_quant\_axelera\_metis\_1             | Personal Protective Equipment (PPE) Detection | 0.3548                                 | 0.6778                                 |
| yolov8n\_relu6\_lp--640x640\_quant\_axelera\_metis\_1              | License Plate Detection                       | 0.5611                                 | 0.8586                                 |
| yolov8n\_relu6\_face\_kpts--640x640\_quant\_axelera\_metis\_1      | Face Detection with Keypoints                 | <p>bbox: 0.2809</p><p>kpts: 0.3885</p> | <p>bbox: 0.3600</p><p>kpts: 0.4504</p> |
| yolov8n\_dota\_obb--1024x1024\_quant\_axelera\_metis\_1            | OBB Detection                                 | 0.4543                                 | 0.6016                                 |
| yolov8n\_relu6\_widerface\_kpts--640x640\_quant\_axelera\_metis\_1 | Face Detection with Five Keypoints            | <p>bbox: 0.2607</p><p>kpts: 0.2317</p> | <p>bbox: 0.7981</p><p>kpts: 0.5391</p> |
| yolov8n\_relu6\_fire\_smoke--640x640\_quant\_axelera\_metis\_1     | Fire & Smoke Detection                        | 0.4155                                 | 0.7383                                 |
| yolo11n\_coco--640x640\_quant\_axelera\_metis\_1                   | COCO Detection                                | 0.3853                                 | 0.5422                                 |
| yolo11n\_coco\_pose--640x640\_quant\_axelera\_metis\_1             | COCO Pose Keypoints                           | <p>bbox: 0.5104</p><p>kpts: 0.4642</p> | <p>bbox: 0.6912</p><p>kpts: 0.7947</p> |
| yolo11n\_coco\_seg--640x640\_quant\_axelera\_metis\_1              | COCO Instance Segmentation                    | <p>bbox: 0.3790</p><p>mask: 0.3099</p> | <p>bbox: 0.5316</p><p>mask: 0.4977</p> |

**Classification**

| Model Name                                                          | Use Case              | Top‑1  | Top‑5 |
| ------------------------------------------------------------------- | --------------------- | ------ | ----- |
| yolov8n\_relu6\_fairface\_gender--256x256\_quant\_axelera\_metis\_1 | Gender Classification | 92.93% | 100%  |


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