kendryte/nncase — Star 增长与排名

nncase

nncase is a neural network compiler for AI accelerators.

Telegram: nncase community Technical Discussion QQ Group: 790699378 . Answer: 人工智能

K230

Install

Linux：
```
pip install nncase nncase-kpu
```

Windows:

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.

Supported operators

benchmark test

kind

model

shape

quant_type(If/W)

nncase_fps

tflite_onnx_result

accuracy

info

Image Classification

mobilenetv2

[1,224,224,3]

u8/u8

600.24

top-1 = 71.3%
top-5 = 90.1%

top-1 = 71.1%
top-5 = 90.0%

dataset(ImageNet 2012, 50000 images)
tflite

resnet50V2

[1,3,224,224]

u8/u8

86.17

top-1 = 75.44%
top-5 = 92.56%

top-1 = 75.11%
top-5 = 92.36%

dataset(ImageNet 2012, 50000 images)
onnx

yolov8s_cls

[1,3,224,224]

u8/u8

130.497

top-1 = 72.2%
top-5 = 90.9%

top-1 = 72.2%
top-5 = 90.8%

dataset(ImageNet 2012, 50000 images)
yolov8s_cls(v8.0.207)

Object Detection

yolov5s_det

[1,3,640,640]

u8/u8

23.645

bbox
mAP50-90 = 0.374
mAP50 = 0.567

bbox
mAP50-90 = 0.369
mAP50 = 0.566

dataset(coco val2017, 5000 images)
yolov5s_det(v7.0 tag, rect=False, conf=0.001, iou=0.65)

yolov8s_det

[1,3,640,640]

u8/u8

9.373

bbox
mAP50-90 = 0.446
mAP50 = 0.612
mAP75 = 0.484

bbox
mAP50-90 = 0.404
mAP50 = 0.593
mAP75 = 0.45

dataset(coco val2017, 5000 images)
yolov8s_det(v8.0.207, rect = False)

Image Segmentation

yolov8s_seg

[1,3,640,640]

u8/u8

7.845

bbox
mAP50-90 = 0.444
mAP50 = 0.606
mAP75 = 0.484
segm
mAP50-90 = 0.371
mAP50 = 0.578
mAP75 = 0.396

bbox
mAP50-90 = 0.444
mAP50 = 0.606
mAP75 = 0.484
segm
mAP50-90 = 0.371
mAP50 = 0.579
mAP75 = 0.397

dataset(coco val2017, 5000 images)
yolov8s_seg(v8.0.207, rect = False, conf_thres = 0.0008)

Pose Estimation

yolov8n_pose_320

[1,3,320,320]

u8/u8

36.066

bbox
mAP50-90 = 0.6
mAP50 = 0.843
mAP75 = 0.654
keypoints
mAP50-90 = 0.358
mAP50 = 0.646
mAP75 = 0.353

bbox
mAP50-90 = 0.6
mAP50 = 0.841
mAP75 = 0.656
keypoints
mAP50-90 = 0.359
mAP50 = 0.648
mAP75 = 0.357

dataset(coco val2017, 2346 images)
yolov8n_pose(v8.0.207, rect = False)

yolov8n_pose_640

[1,3,640,640]

u8/u8

10.88

bbox
mAP50-90 = 0.694
mAP50 = 0.909
mAP75 = 0.776
keypoints
mAP50-90 = 0.509
mAP50 = 0.798
mAP75 = 0.544

bbox
mAP50-90 = 0.694
mAP50 = 0.909
mAP75 = 0.777
keypoints
mAP50-90 = 0.508
mAP50 = 0.798
mAP75 = 0.54

dataset(coco val2017, 2346 images)
yolov8n_pose(v8.0.207, rect = False)

yolov8s_pose

[1,3,640,640]

u8/u8

5.568

bbox
mAP50-90 = 0.733
mAP50 = 0.925
mAP75 = 0.818
keypoints
mAP50-90 = 0.605
mAP50 = 0.857
mAP75 = 0.666

bbox
mAP50-90 = 0.734
mAP50 = 0.925
mAP75 = 0.819
keypoints
mAP50-90 = 0.604
mAP50 = 0.859
mAP75 = 0.669

dataset(coco val2017, 2346 images)
yolov8s_pose(v8.0.207, rect = False)

Demo

eye gaze	space_resize	face pose

K210/K510

Supported operators

Features

Supports multiple inputs and outputs and multi-branch structure
Static memory allocation, no heap memory acquired
Operators fusion and optimizations
Support float and quantized uint8 inference
Support post quantization from float model with calibration dataset
Flat model with zero copy loading

Architecture

nncase arch

Build from source

It is recommended to install nncase directly through pip. At present, the source code related to k510 and K230 chips is not open source, so it is not possible to use nncase-K510 and nncase-kpu (K230) directly by compiling source code.

If there are operators in your model that nncase does not yet support, you can request them in the issue or implement them yourself and submit the PR. Later versions will be integrated, or contact us to provide a temporary version. Here are the steps to compile nncase.

git clone https://github.com/kendryte/nncase.git
cd nncase
mkdir build && cd build

# Use Ninja
cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install
ninja && ninja install

# Use make
cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install
make && make install

Resources

Canaan developer community

Canaan developer community contains all resources related to K210, K510, and K230.

资料下载 --> Pre-compiled images available for the development boards corresponding to the three chips.
文档 --> Documents corresponding to the three chips.
模型库 --> Examples and code for industrial, security, educational and other scenarios that can be run on the K210 and K230.
模型训练 --> The model training platform for K210 and K230 supports the training of various scenarios.

nncase

K230

Install

Supported operators

benchmark test

Demo

K210/K510

Supported operators

Features

Architecture

Build from source

Resources

Canaan developer community

Bilibili

K210 related repo

K230 related repo