nncase 2.8.2

A neural network compiler for AI accelerators

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nncase is a neural network compiler for AI accelerators.

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

[TOC]

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K230

• Usage
• FAQ
• Example
• Colab run
• Version relationship between nncase and K230_SDK
• update nncase runtime library in SDK

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

• TFLite ops
• Caffe ops
• ONNX ops

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

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K210/K510

• Usage
• FAQ
• Example

Supported operators

• TFLite ops
• Caffe ops
• ONNX ops

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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

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Architecture

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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

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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.

Bilibili

• Canaan AI tutorial and application demonstration

K210 related repo

• K210_Yolo_framework
• Shts!'s Blog (Japanese)
• Examples

K230 related repo

• C: K230_SDK
  • Documents
  • K230 end-to-end tutorial
• MicroPython: Canmv_k230
  • Documents
  • Examples

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