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This version of YOLO11-OBB (Oriented Bounding Box) has been converted to run on the Axera NPU using w8a16 quantization. It is optimized for detecting rotated objects such as ships, harbors, vehicles, and other...
Fuente del modelo
Descripción de la fuente
This version of YOLO11-OBB (Oriented Bounding Box) has been converted to run on the Axera NPU using w8a16 quantization. It is optimized for detecting rotated objects such as ships, harbors, vehicles, and other DOTA-style targets with oriented bounding boxes.
Compatible with Pulsar2 version: 6.0.
For those who are interested in model conversion, you can try to export axmodel through:
Fuentes
1 fuenteVerificado 27 sept
Artefactos del modelo
10 artefactosExtractos de fuentes
2 extractosThis repository currently provides axmodels for the following Axera platforms only:
The converted axmodels are organized by target platform:
yolo11_obb/
├── 650/
│ ├── yolo11n-obb_640x640_npu1.axmodel
│ ├── yolo11n-obb_640x640_npu3.axmodel
│ ├── yolo11s-obb_640x640_npu1.axmodel
│ ├── yolo11s-obb_640x640_npu3.axmodel
│ ├── yolo11m-obb_640x640_npu1.axmodel
│ ├── yolo11m-obb_640x640_npu3.axmodel
│ ├── yolo11l-obb_640x640_npu1.axmodel
│ ├── yolo11l-obb_640x640_npu3.axmodel
│ ├── yolo11x-obb_640x640_npu1.axmodel
│ └── yolo11x-obb_640x640_npu3.axmodel
├── 637/
│ ├── yolo11n-obb_640x640_npu1.axmodel
│ ├── yolo11s-obb_640x640_npu1.axmodel
│ ├── yolo11m-obb_640x640_npu1.axmodel
│ ├── yolo11l-obb_640x640_npu1.axmodel
│ └── yolo11x-obb_640x640_npu1.axmodel
├── ax_infer.py
├── boats.jpg
└── result_yolo11_obb_ax.jpg
Latency data is left blank and can be filled in after benchmark testing.
| Model | Latency(ms) npu1 | Latency(ms) npu3 |
|---|---|---|
| yolo11n-obb | 3.491 | 1.383 |
| yolo11s-obb | 9.008 | 3.240 |
| yolo11m-obb | 26.086 | 8.958 |
| yolo11l-obb | 33.724 | 11.496 |
| yolo11x-obb | 73.796 | 25.168 |
| Model | Latency(ms) |
|---|---|
| yolo11n-obb | 4.191 |
| yolo11s-obb | 11.068 |
| yolo11m-obb | 27.316 |
| yolo11l-obb | 35.625 |
| yolo11x-obb | 79.141 |
Download all files from this repository to the device, then choose the axmodel that matches your target platform.
wget https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3.rc2/axengine-0.1.3-py3-none-any.whl
pip install axengine-0.1.3-py3-none-any.whl
Input image:
Run with an AX650N/AX8850 model:
python3 ax_infer.py -m 650/yolo11m-obb_640x640_npu3.axmodel -i boats.jpg
Run with an AX637 model:
python3 ax_infer.py -m 637/yolo11m-obb_640x640_npu1.axmodel -i boats.jpg
Example output from AX637:
root@ax637:~/11obb# python3 ax_infer.py -m yolo11m-obb_640x640_npu3.axmodel -i boats.jpg
[INFO] Available providers: ['AxEngineExecutionProvider']
[INFO] Using provider: AxEngineExecutionProvider
[INFO] Chip type: ChipType.M57H
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Engine version: 2.12.0s
[INFO] Model type: 0 (single core)
[INFO] Compiler version: 6.0 93b95f7f
Load model: 844.4 ms
Forward+post: 194.9 ms
Found 167 oriented objects.
ship conf=0.84 cx=1707.6 cy=792.9 w=105.7 h=34.9 theta=+26.0
ship conf=0.84 cx=1790.4 cy=573.8 w=117.8 h=36.5 theta=+24.5
ship conf=0.84 cx=1723.6 cy=760.6 w=108.8 h=31.4 theta=+24.5
ship conf=0.84 cx=1782.8 cy=620.8 w=112.0 h=35.7 theta=+25.0
ship conf=0.82 cx=1487.2 cy=694.4 w=97.2 h=31.3 theta=+21.0
...
harbor conf=0.29 cx=824.3 cy=184.2 w=230.5 h=521.3 theta=+24.0
ship conf=0.29 cx=1219.5 cy=246.9 w=85.7 h=24.0 theta=+23.0
ship conf=0.29 cx=1652.9 cy=276.2 w=80.3 h=26.1 theta=+20.5
ship conf=0.29 cx=1190.1 cy=307.0 w=91.3 h=28.6 theta=+21.0
harbor conf=0.27 cx=1535.8 cy=388.2 w=221.8 h=953.5 theta=+20.5
ship conf=0.26 cx=1517.0 cy=274.2 w=84.8 h=24.2 theta=+21.5
ship conf=0.26 cx=1522.8 cy=252.3 w=89.5 h=29.9 theta=+19.5
Saved: result_yolo11_obb_ax.jpg
Output image:
yolo11m-obb_640x640_raw.onnx
onnx · 79,9 MB · SHA-256 48d0e9b40d07…d725 · Hugging Face
--- license: agpl-3.0 language: - en base_model: - YOLO11 pipeline_tag: object-detection tags: - Axera - YOLO11-OBB - NPU - Ultralytics - Oriented Object Detection - Rotated Bounding Box --- # YOLO11-OBB This version of **YOLO11-OBB** (Oriented Bounding Box) has been converted to run on the Axera NPU using **w8a16** quantization. It is optimized for detecting rotated objects such as ships, harbors, vehicles, and other DOTA-style targets with oriented bounding boxes. Compatible with Pulsar2 version: 6.0. ## Convert tools links For those who are interested in model conversion, you can try to export axmodel through: - [The repo of AXera Platform](https://github.com/AXERA-TECH/ax-samples), where you can get the detailed guide. - [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html) ## Support Platform This repository currently provides axmodels for the following Axera platforms only: - **AX650N/AX8850** - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html) - [M.2 Accelerator card](https://docs.m5stack.com/en/ai_hardware/LLM-8850_Card) - **AX637** ## Model files The converted axmodels are organized by target platform: ```text yolo11_obb/ ├── 650/ │ ├── yolo11n-obb_640x640_npu1.axmodel │ ├── yolo11n-obb_640x640_npu3.axmodel │ ├── yolo11s-obb_640x640_npu1.axmodel │ ├── yolo11s-obb_640x640_npu3.axmodel │ ├── yolo11m-obb_640x640_npu1.axmodel │ ├── yolo11m-obb_640x640_npu3.axmodel │ ├── yolo11l-obb_640x640_npu1.axmodel │ ├── yolo11l-obb_640x640_npu3.axmodel │ ├── yolo11x-obb_640x640_npu1.axmodel │ └── yolo11x-obb_640x640_npu3.axmodel ├── 637/ │ ├── yolo11n-obb_640x640_npu1.axmodel │ ├── yolo11s-obb_640x640_npu1.axmodel │ ├── yolo11m-obb_640x640_npu1.axmodel │ ├── yolo11l-obb_640x640_npu1.axmodel │ └── yolo11x-obb_640x640_npu1.axmodel ├── ax_infer.py ├── boats.jpg └── result_yolo11_obb_ax.jpg ``` ## Performance Statistics Latency data is left blank and can be filled in after benchmark testing. ### AX650N/AX8850 | Model | Latency(ms) npu1 | Latency(ms) npu3 | | :--- | :---: | :---: | | **yolo11n-obb** | 3.491 | 1.383 | | **yolo11s-obb** | 9.008 | 3.240 | | **yolo11m-obb** | 26.086 | 8.958 | | **yolo11l-obb** | 33.724 | 11.496 | | **yolo11x-obb** | 73.796 | 25.168 | ### AX637 | Model | Latency(ms) | | :--- | :---: | | **yolo11n-obb** | 4.191 | | **yolo...
Source context: 12 downloads · 0 likes · Pipeline object-detection · Repo AXERA-TECH/yolo11-obb