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Open deep learning compiler stack for Kendryte AI accelerators ✨
| Date | Stars |
|---|---|
| 2026-07-31 | 898 |
| 2026-08-04 | 898 |
| 2026-08-06 | 898 |
| 2026-08-13 | 897 |
| 2026-08-17 | 897 |
| 2026-08-18 | 897 |
| 2026-08-23 | 898 |
| 2026-08-25 | 899 |
| 2026-08-29 | 898 |
| 2026-09-01 | 897 |
| 2026-09-02 | 898 |
| 2026-09-06 | 898 |
| 2026-09-11 | 897 |
| 2026-09-12 | 897 |
| 2026-09-16 | 897 |
| 2026-09-18 | 898 |
| 2026-09-19 | 898 |
| 2026-09-20 | 898 |
Today
— stars today
This week
+1 stars this week
This month
+1 stars this month
Momentum
0.0
growth rate 0.11%/day
<div align="center">
<img src="docs/logo.png" width="400" alt="nncase" />
</div>
[](https://github.com/kendryte/nncase) [](https://gitee.com/kendryte/nncase) [](https://github.com/kendryte/nncase/releases)
[切换中文](docs/readme_ZH.md)
`nncase` is a neural network compiler for AI accelerators.
Telegram: [nncase community](https://t.me/joinchat/PPcEPZMLaTViNDI1)
Technical Discussion QQ Group: 790699378 . Answer: 人工智能
---
## K230
- [Usage](./docs/USAGE_v2_EN.md)
- [FAQ](./docs/FAQ_EN.md)
- [Example](./examples/user_guide/k230_simulate-EN.ipynb)
- [Colab run](https://colab.research.google.com/drive/1m8TTree096m5VHmq-Uc60gXyltVCgnRb?usp=sharing)
- [ *Version relationship between `nncase` and `K230_SDK`* ](https://developer.canaan-creative.com/k230/dev/zh/03_other/K230_SDK_nncase%E7%89%88%E6%9C%AC%E5%AF%B9%E5%BA%94%E5%85%B3%E7%B3%BB.html#k230sdknncase)
- [update nncase runtime library in SDK](https://developer.canaan-creative.com/k230/dev/zh/03_other/K230_SDK%E6%9B%B4%E6%96%B0nncase%E8%BF%90%E8%A1%8C%E6%97%B6%E5%BA%93%E6%8C%87%E5%8D%97.html)
### Install
- Linux：
```shell
pip install nncase nncase-kpu
```
- Windows:
```shell
1. pip install nncase
2. Download `nncase_kpu-2.x.x-py2.py3-none-win_amd64.whl` in below link.
3. pip install nncase_kpu-2.x.x-py2.py3-none-win_amd64.whl
```
All version of `nncase` and `nncase-kpu` in [Release](https://github.com/kendryte/nncase/releases).
### Supported operators
- [TFLite ops](./docs/tflite_ops.md)
- [Caffe ops](./docs/caffe_ops.md)
- [ONNX ops](./docs/onnx_ops.md)
### benchmark test
<table>
<tr> <th>kind</th> <th> model </th><th> shape </th><th> quant_type(If/W) </th><th> nncase_fps </th><th> tflite_onnx_result </th><th> accuracy </th><th> info </th></tr>
<tr>
<td rowspan='3'>Image Classification</td>
<td>mobilenetv2 </td><td> [1,224,224,3] </td><td> u8/u8 </td><td> 600.24 </td><td> top-1 = 71.3%<br/>top-5 = 90.1% </td><td> top-1 = 71.1%<br/>top-5 = 90.0% </td><td> dataset(ImageNet 2012, 50000 images)<br/> tflite </td></tr>
<tr><td>resnet50V2 </td><td> [1,3,224,224] </td><td> u8/u8 </td><td> 86.17 </td><td> top-1 = 75.44%<br/>top-5 = 92.56% </td><td> top-1 = 75.11% <br/> top-5 = 92.36% </td><td> dataset(ImageNet 2012, 50000 images)<br/> onnx</td></tr>
<tr><td>yolov8s_cls </td><td> [1,3,224,224] </td><td> u8/u8 </td><td> 130.497 </td><td> top-1 = 72.2%<br/>top-5 = 90.9% </td><td> top-1 = 72.2%<br/>top-5 = 90.8% </td><td> dataset(ImageNet 2012, 50000 images)<br/> yolov8s_cls(v8.0.207)</td></tr>
<tr>
<td rowspan='2'>Object Detection</td>
<td>yolov5s_det </td><td> [1,3,640,640] </td><td> u8/u8 </td><td> 23.645 </td><td> bbox<br/>mAP50-90 = 0.374<br/>mAP50 = 0.567 </td><td> bbox<br/>mAP50-90 = 0.369<br/>mAP50 = 0.566</td><td>dataset(coco val2017, 5000 images)<br/>yolov5s_det(v7.0 tag, rect=False, conf=0.001, iou=0.65)</td></tr>
<tr><td>yolov8s_det </td><td> [1,3,640,640] </td><td> u8/u8 </td><td> 9.373 </td><td> bbox<br/>mAP50-90 = 0.446<br/>mAP50 = 0.612<br/>mAP75 = 0.484 </td><td> bbox<br/>mAP50-90 = 0.404<br/>mAP50 = 0.593<br/>mAP75 = 0.45</td><td>dataset(coco val2017, 5000 images)<br/>yolov8s_det(v8.0.207, rect = False)</td></tr>
<tr>
<td rowspan='1'>Image Segmentation</td>
<td>yolov8s_seg </td><td> [1,3,640,640] </td><td> u8/u8 </td><td> 7.845 </td><td> bbox<br/>mAP50-90 = 0.444<br/>mAP50 = 0.606<br/>mAP75 = 0.484<br/>segm<br/>mAP50-90 = 0.371<br/>mAP50 = 0.578<br/>mAP75 = 0.396 </td><td> bbox<br/>mAP50-90 = 0.444<br/>mAP50 = 0.Excerpt of 9,469 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:29ade357f9844070, topic:deep-learning, topic:neural-network
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