ModelAufgelistetExtern

YOLOv8 Seg

This version of YOLOv8-Seg has been converted to run on the Axera NPU using w8a16 quantization.

Quellehuggingface.co/AXERA-TECH/YOLOv8-Seg/tree/6cb7ad455fa1bcf65a3a48bb9c0541625fdf2999

ArtOtherBasismodellUltralytics/YOLOv8VersionRevision 6cb7ad4DBewertung

Modellquelle

Art: Other
Dateigröße: 13,1–274 MB
Basismodell: Ultralytics/YOLOv8
Version: vRevision 6cb7ad4
Aktualisiert: 21. Okt. 2026
Downloads: 33
Quelle: Hugging Face

Quellenbeschreibung

YOLOv8-Seg

This version of YOLOv8-Seg has been converted to run on the Axera NPU using w8a16 quantization.

This model has been optimized with the following LoRA:

Compatible with Pulsar2 version: 5.0

Convert tools links:

For those who are interested in model conversion, you can try to export axmodel through

The repo of AXera Platform, which you can get the detial of guide
Pulsar2 Link, How to Convert ONNX to axmodel

Support Platform

AX650N/AX8850
- M4N-Dock(爱芯派Pro)
- M.2 Accelerator card
AX630C
AX615
AX637

Performance Statistics

AX650N

Model	Latency(ms) npu1	Latency(ms) npu3
yolov8n-seg	5.096	1.847
yolov8s-seg	13.724	4.735
yolov8m-seg	34.582	11.912
yolov8l-seg	65.275	22.312
yolov8x-seg	110.208	36.147

AX630C

Model	Latency(ms) npu1	Latency(ms) npu2
yolov8n-seg	18.561	12.036
yolov8s-seg	38.493	30.259
yolov8m-seg	94.779	69.217
yolov8l-seg	179.964	121.261
yolov8x-seg	296.999	190.418

AX615

Model	Latency(ms) npu1	Latency(ms) npu2
yolov8n-seg	22.024	13.867
yolov8s-seg	62.068	35.333
yolov8m-seg	160.450	86.186

AX637

Model	Latency(ms) npu1
yolov8n-seg	6.175
yolov8s-seg	15.654
yolov8m-seg	35.612
yolov8l-seg	67.062
yolov8x-seg	104.862

How to use

Download all files from this repository to the device

Inference

Input image:

Inference with AX650 Host, such as M4N-Dock(爱芯派Pro)

(base) root@ax650:~/ax650seg# python3 ax_infer.py --model-path yolov8m-seg_640x640_npu3.axmodel --test-img bus.jpg
[INFO] Using provider: AxEngineExecutionProvider
[INFO] Chip type: ChipType.MC50
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Engine version: 2.12.0s
[INFO] Model type: 2 (triple core)
[INFO] Compiler version: 6.0-dirty a498e20d-dirty
[YOLOv8-Seg] [15:38:19.398] [DEBUG] Load model time = 592.02 ms
[YOLOv8-Seg] [15:38:19.433] [DEBUG] Pre-process time = 8.17 ms
[YOLOv8-Seg] [15:38:19.472] [DEBUG] Forward time = 38.19 ms
[YOLOv8-Seg] [15:38:19.482] [DEBUG] Post-process time = 9.47 ms
[YOLOv8-Seg] [15:38:19.483] [DEBUG] Proto shape: (32, 160, 160)
[YOLOv8-Seg] [15:38:19.534] [INFO] Draw Results (6 objects):
[YOLOv8-Seg] [15:38:19.535] [INFO] (13, 230, 803, 736) -> bus: 0.93
[YOLOv8-Seg] [15:38:19.580] [INFO] (49, 398, 244, 904) -> person: 0.91
[YOLOv8-Seg] [15:38:19.599] [INFO] (222, 397, 345, 860) -> person: 0.89
[YOLOv8-Seg] [15:38:19.616] [INFO] (667, 395, 810, 880) -> person: 0.88
[YOLOv8-Seg] [15:38:19.632] [INFO] (0, 551, 78, 866) -> person: 0.59
[YOLOv8-Seg] [15:38:19.647] [INFO] (137, 472, 148, 504) -> tie: 0.37
[YOLOv8-Seg] [15:38:19.685] [INFO] Saved to result_yolov8_seg.jpg

Output image:

Quellen

1 Quelle

Hugging FacePrimärVersion 6cb7ad455fa1bcf65a3a48bb9c0541625fdf2999

huggingface.co/AXERA-TECH/YOLOv8-Seg/tree/6cb7ad455fa1bcf65a3a48bb9c0541625fdf2999

Verifiziert 21. Okt.

Modellartefakte

5 Artefakte

yolov8l-seg_640x640.onnx

onnx · 175 MB · SHA-256 2b317e94a6ac…c175 · Hugging Face

Herunterladen

yolov8m-seg_640x640.onnx

onnx · 104 MB · SHA-256 b2381143275c…8faf · Hugging Face

Herunterladen

yolov8n-seg_640x640.onnx

onnx · 13,1 MB · SHA-256 924de98d6bb9…74b3 · Hugging Face

Herunterladen

yolov8s-seg_640x640.onnx

onnx · 45,1 MB · SHA-256 330f3943b419…d06a · Hugging Face

Herunterladen

yolov8x-seg_640x640.onnx

onnx · 274 MB · SHA-256 d6b88d24056e…92fb · Hugging Face

Herunterladen

Quellenauszüge

3 Auszüge

Readme5. Aug.huggingface.co/AXERA-TECH/YOLOv8-Seg

--- license: mit language: - en base_model: - Ultralytics/YOLOv8 pipeline_tag: object-detection tags: - Ultralytics - YOLOv8 - YOLOv8-Seg --- # YOLOv8-Seg This version of YOLOv8-Seg has been converted to run on the Axera NPU using **w8a16** quantization. This model has been optimized with the following LoRA: Compatible with Pulsar2 version: 5.0 ## 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), which you can get the detial of guide - [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html) ## Support Platform - **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) - **AX630C** - [爱芯派2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html) - [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM) - [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit) - **AX615** - **AX637** ### Performance Statistics #### AX650N | Model | Latency(ms) npu1 | Latency(ms) npu3 | | :--- | :---: | :---: | | **yolov8n-seg** | 5.096 | 1.847 | | **yolov8s-seg** | 13.724 | 4.735 | | **yolov8m-seg** | 34.582 | 11.912 | | **yolov8l-seg** | 65.275 | 22.312 | | **yolov8x-seg** | 110.208 | 36.147 | #### AX630C | Model | Latency(ms) npu1 | Latency(ms) npu2 | | :--- | :---: | :---: | | **yolov8n-seg** | 18.561 | 12.036 | | **yolov8s-seg** | 38.493 | 30.259 | | **yolov8m-seg** | 94.779 | 69.217 | | **yolov8l-seg** | 179.964 | 121.261 | | **yolov8x-seg** | 296.999 | 190.418 | #### AX615 | Model | Latency(ms) npu1 | Latency(ms) npu2 | | :--- | :---: | :---: | | **yolov8n-seg** | 22.024 | 13.867 | | **yolov8s-seg** | 62.068 | 35.333 | | **yolov8m-seg** | 160.450 | 86.186 | #### AX637 | Model | Latency(ms) npu1 | | :--- | :---: | | **yolov8n-seg** | 6.175 | | **yolov8s-seg** | 15.654 | | **yolov8m-seg** | 35.612 | | **yolov8l-seg** | 67.062 | | **yolov8x-seg** | 104.862 | ## How to use Download all files from this repository to the device ### Inference Input image: ![](./bus.jpg) #### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) ``` (base) root@ax650:~/ax650seg# python3 ax_infer.py --model...

Readme16. Julihuggingface.co/AXERA-TECH/YOLOv8-Seg

--- license: mit language: - en base_model: - Ultralytics/YOLOv8 pipeline_tag: object-detection tags: - Ultralytics - YOLOv8 - YOLOv8-Seg --- # YOLOv8-Seg This version of YOLOv8-Seg has been converted to run on the Axera NPU using **w8a16** quantization. This model has been optimized with the following LoRA: Compatible with Pulsar2 version: 5.0 ## 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), which you can get the detial of guide - [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html) ## Support Platform - **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) - **AX630C** - [爱芯派2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html) - [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM) - [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit) - **AX615** - **AX637** ### Performance Statistics #### AX650N | Model | Latency(ms) npu1 | Latency(ms) npu3 | | :--- | :---: | :---: | | **yolo26n-seg** | 5.096 | 1.847 | | **yolo26s-seg** | 13.724 | 4.735 | | **yolo26m-seg** | 34.582 | 11.912 | | **yolo26l-seg** | 65.275 | 22.312 | | **yolo26x-seg** | 110.208 | 36.147 | #### AX630C | Model | Latency(ms) npu1 | Latency(ms) npu2 | | :--- | :---: | :---: | | **yolo26n-seg** | 18.561 | 12.036 | | **yolo26s-seg** | 38.493 | 30.259 | | **yolo26m-seg** | 94.779 | 69.217 | | **yolo26l-seg** | 179.964 | 121.261 | | **yolo26x-seg** | 296.999 | 190.418 | #### AX615 | Model | Latency(ms) npu1 | Latency(ms) npu2 | | :--- | :---: | :---: | | **yolo26n-seg** | 22.024 | 13.867 | | **yolo26s-seg** | 62.068 | 35.333 | | **yolo26m-seg** | 160.450 | 86.186 | #### AX637 | Model | Latency(ms) npu1 | | :--- | :---: | | **yolo26n-seg** | 6.175 | | **yolo26s-seg** | 15.654 | | **yolo26m-seg** | 35.612 | | **yolo26l-seg** | 67.062 | | **yolo26x-seg** | 104.862 | ## How to use Download all files from this repository to the device ### Inference Input image: ![](./bus.jpg) #### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) ``` (base) root@ax650:~/ax650seg# python3 ax_infer.py --model...

Readme11. Julihuggingface.co/AXERA-TECH/YOLOv8-Seg

--- license: mit language: - en base_model: - Ultralytics/YOLOv8 pipeline_tag: object-detection tags: - Ultralytics - YOLOv8 - YOLOv8-Seg --- # YOLOv8-Seg This version of YOLOv8-Seg has been converted to run on the Axera NPU using **w8a16** quantization. This model has been optimized with the following LoRA: Compatible with Pulsar2 version: 5.0 ## 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), which you can get the detial of guide - [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html) ## Support Platform - **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) - **AX630C** - [爱芯派2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html) - [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM) - [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit) - **AX615** - **AX637** ### Performance Statistics #### AX650N | Model | Latency(ms) npu1 | Latency(ms) npu3 | | :--- | :---: | :---: | | **yolo26n-seg** | - | - | | **yolo26s-seg** | - | - | | **yolo26m-seg** | - | - | | **yolo26l-seg** | - | - | | **yolo26x-seg** | - | - | #### AX630C | Model | Latency(ms) npu1 | Latency(ms) npu2 | | :--- | :---: | :---: | | **yolo26n-seg** | - | - | | **yolo26s-seg** | - | - | | **yolo26m-seg** | - | - | | **yolo26l-seg** | - | - | | **yolo26x-seg** | - | - | #### AX615 | Model | Latency(ms) npu1 | Latency(ms) npu2 | | :--- | :---: | :---: | | **yolo26n-seg** | - | - | | **yolo26s-seg** | - | - | | **yolo26m-seg** | - | - | | **yolo26l-seg** | - | - | | **yolo26x-seg** | - | - | #### AX637 | Model | Latency(ms) npu1 | | :--- | :---: | | **yolo26n-seg** | - | | **yolo26s-seg** | - | | **yolo26m-seg** | - | | **yolo26l-seg** | - | | **yolo26x-seg** | - | ## How to use Download all files from this repository to the device ### Inference Input image: ![](./bus.jpg) #### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) ``` (base) root@ax650:~/ax650seg# python3 ax_infer.py --model-path yolov8m-seg_640x640_npu3.axmodel --test-img bus.jpg [INFO] Using provider: AxEngineExecutionProv...