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nncase
Open deep learning compiler stack for Kendryte AI accelerators ✨
Stars: 757
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nncase is a neural network compiler for AI accelerators that supports multiple inputs and outputs, static memory allocation, operators fusion and optimizations, float and quantized uint8 inference, post quantization from float model with calibration dataset, and flat model with zero copy loading. It can be installed via pip and supports TFLite, Caffe, and ONNX ops. Users can compile nncase from source using Ninja or make. The tool is suitable for tasks like image classification, object detection, image segmentation, pose estimation, and more.
README:
nncase
is a neural network compiler for AI accelerators.
Telegram: nncase community Technical Discussion QQ Group: 790699378 . Answer: 人工智能
- Usage
- FAQ
- Example
- Colab run
- Version relationship between
nncase
andK230_SDK
- update nncase runtime library in SDK
-
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.
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) |
eye gaze | space_resize | face pose |
---|---|---|
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- 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
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
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.
- C: K230_SDK
- MicroPython: Canmv_k230
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open-ai
Open AI is a powerful tool for artificial intelligence research and development. It provides a wide range of machine learning models and algorithms, making it easier for developers to create innovative AI applications. With Open AI, users can explore cutting-edge technologies such as natural language processing, computer vision, and reinforcement learning. The platform offers a user-friendly interface and comprehensive documentation to support users in building and deploying AI solutions. Whether you are a beginner or an experienced AI practitioner, Open AI offers the tools and resources you need to accelerate your AI projects and stay ahead in the rapidly evolving field of artificial intelligence.
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Qwen-TensorRT-LLM
Qwen-TensorRT-LLM is a project developed for the NVIDIA TensorRT Hackathon 2023, focusing on accelerating inference for the Qwen-7B-Chat model using TRT-LLM. The project offers various functionalities such as FP16/BF16 support, INT8 and INT4 quantization options, Tensor Parallel for multi-GPU parallelism, web demo setup with gradio, Triton API deployment for maximum throughput/concurrency, fastapi integration for openai requests, CLI interaction, and langchain support. It supports models like qwen2, qwen, and qwen-vl for both base and chat models. The project also provides tutorials on Bilibili and blogs for adapting Qwen models in NVIDIA TensorRT-LLM, along with hardware requirements and quick start guides for different model types and quantization methods.
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dl_model_infer
This project is a c++ version of the AI reasoning library that supports the reasoning of tensorrt models. It provides accelerated deployment cases of deep learning CV popular models and supports dynamic-batch image processing, inference, decode, and NMS. The project has been updated with various models and provides tutorials for model exports. It also includes a producer-consumer inference model for specific tasks. The project directory includes implementations for model inference applications, backend reasoning classes, post-processing, pre-processing, and target detection and tracking. Speed tests have been conducted on various models, and onnx downloads are available for different models.
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joliGEN
JoliGEN is an integrated framework for training custom generative AI image-to-image models. It implements GAN, Diffusion, and Consistency models for various image translation tasks, including domain and style adaptation with conservation of semantics. The tool is designed for real-world applications such as Controlled Image Generation, Augmented Reality, Dataset Smart Augmentation, and Synthetic to Real transforms. JoliGEN allows for fast and stable training with a REST API server for simplified deployment. It offers a wide range of options and parameters with detailed documentation available for models, dataset formats, and data augmentation.
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ai-edge-torch
AI Edge Torch is a Python library that supports converting PyTorch models into a .tflite format for on-device applications on Android, iOS, and IoT devices. It offers broad CPU coverage with initial GPU and NPU support, closely integrating with PyTorch and providing good coverage of Core ATen operators. The library includes a PyTorch converter for model conversion and a Generative API for authoring mobile-optimized PyTorch Transformer models, enabling easy deployment of Large Language Models (LLMs) on mobile devices.
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awesome-RK3588
RK3588 is a flagship 8K SoC chip by Rockchip, integrating Cortex-A76 and Cortex-A55 cores with NEON coprocessor for 8K video codec. This repository curates resources for developing with RK3588, including official resources, RKNN models, projects, development boards, documentation, tools, and sample code.
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cl-waffe2
cl-waffe2 is an experimental deep learning framework in Common Lisp, providing fast, systematic, and customizable matrix operations, reverse mode tape-based Automatic Differentiation, and neural network model building and training features accelerated by a JIT Compiler. It offers abstraction layers, extensibility, inlining, graph-level optimization, visualization, debugging, systematic nodes, and symbolic differentiation. Users can easily write extensions and optimize their networks without overheads. The framework is designed to eliminate barriers between users and developers, allowing for easy customization and extension.
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TensorRT-Model-Optimizer
The NVIDIA TensorRT Model Optimizer is a library designed to quantize and compress deep learning models for optimized inference on GPUs. It offers state-of-the-art model optimization techniques including quantization and sparsity to reduce inference costs for generative AI models. Users can easily stack different optimization techniques to produce quantized checkpoints from torch or ONNX models. The quantized checkpoints are ready for deployment in inference frameworks like TensorRT-LLM or TensorRT, with planned integrations for NVIDIA NeMo and Megatron-LM. The tool also supports 8-bit quantization with Stable Diffusion for enterprise users on NVIDIA NIM. Model Optimizer is available for free on NVIDIA PyPI, and this repository serves as a platform for sharing examples, GPU-optimized recipes, and collecting community feedback.
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depthai
This repository contains a demo application for DepthAI, a tool that can load different networks, create pipelines, record video, and more. It provides documentation for installation and usage, including running programs through Docker. Users can explore DepthAI features via command line arguments or a clickable QT interface. Supported models include various AI models for tasks like face detection, human pose estimation, and object detection. The tool collects anonymous usage statistics by default, which can be disabled. Users can report issues to the development team for support and troubleshooting.