All Projects → tenglike1997 → Onnxruntime Projects

tenglike1997 / Onnxruntime Projects

Code for some onnxruntime projects

Labels

Projects that are alternatives of or similar to Onnxruntime Projects

Neoml
Machine learning framework for both deep learning and traditional algorithms
Stars: ✭ 568 (+628.21%)
Mutual labels:  onnx
Tract
Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference
Stars: ✭ 899 (+1052.56%)
Mutual labels:  onnx
Advbox
Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.
Stars: ✭ 1,055 (+1252.56%)
Mutual labels:  onnx
Pinto model zoo
A repository that shares tuning results of trained models generated by TensorFlow / Keras. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. TensorFlow Lite. OpenVINO. CoreML. TensorFlow.js. TF-TRT. MediaPipe. ONNX. [.tflite,.h5,.pb,saved_model,tfjs,tftrt,mlmodel,.xml/.bin, .onnx]
Stars: ✭ 634 (+712.82%)
Mutual labels:  onnx
Deepo
Setup and customize deep learning environment in seconds.
Stars: ✭ 6,145 (+7778.21%)
Mutual labels:  onnx
Onnx Tensorflow
Tensorflow Backend for ONNX
Stars: ✭ 846 (+984.62%)
Mutual labels:  onnx
Onnxruntime
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Stars: ✭ 5,910 (+7476.92%)
Mutual labels:  onnx
Onnx Scala
An ONNX (Open Neural Network eXchange) API and Backend for Typeful, Functional Deep Learning in Scala
Stars: ✭ 68 (-12.82%)
Mutual labels:  onnx
Multi Model Server
Multi Model Server is a tool for serving neural net models for inference
Stars: ✭ 770 (+887.18%)
Mutual labels:  onnx
Onnx R
R Interface to Open Neural Network Exchange (ONNX)
Stars: ✭ 31 (-60.26%)
Mutual labels:  onnx
Windows Machine Learning
Samples and Tools for Windows ML.
Stars: ✭ 663 (+750%)
Mutual labels:  onnx
Translate
Translate - a PyTorch Language Library
Stars: ✭ 684 (+776.92%)
Mutual labels:  onnx
Yolov3
YOLOv3 in PyTorch > ONNX > CoreML > TFLite
Stars: ✭ 8,159 (+10360.26%)
Mutual labels:  onnx
Mmdnn
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
Stars: ✭ 5,472 (+6915.38%)
Mutual labels:  onnx
Onnx tflite yolov3
A Conversion tool to convert YOLO v3 Darknet weights to TF Lite model (YOLO v3 PyTorch > ONNX > TensorFlow > TF Lite), and to TensorRT (YOLO v3 Pytorch > ONNX > TensorRT).
Stars: ✭ 52 (-33.33%)
Mutual labels:  onnx
Onnxmltools
ONNXMLTools enables conversion of models to ONNX
Stars: ✭ 476 (+510.26%)
Mutual labels:  onnx
Tensorflow Onnx
Convert TensorFlow models to ONNX
Stars: ✭ 900 (+1053.85%)
Mutual labels:  onnx
Gluon2pytorch
Gluon to PyTorch deep neural network model converter
Stars: ✭ 70 (-10.26%)
Mutual labels:  onnx
Pytorch Onnx Tensorrt
A set of tool which would make your life easier with Tensorrt and Onnxruntime. This Repo is designed for YoloV3
Stars: ✭ 66 (-15.38%)
Mutual labels:  onnx
Mlnet Workshop
ML.NET Workshop to predict car sales prices
Stars: ✭ 29 (-62.82%)
Mutual labels:  onnx

onnxruntime projects

Introduction

This repository include codes for some onnxruntime projects,such as classification, segmentation, detection, style transfer and super resolution.

Onnxruntime

ONNX Runtime is a performance-focused complete scoring engine for Open Neural Network Exchange (ONNX) models, with an open extensible architecture to continually address the latest developments in AI and Deep Learning. In my repository,onnxruntime.dll have been compiled. You can download it and see specific information about onnxruntime in https://github.com/microsoft/onnxruntime.

Projects

The programming language is C++ and The platform is Visual Studio. I have finished some projects based on onnxruntime official samples. The link have been mentioned afore. Also, you can download some onnx models in https://github.com/onnx/models. If necessary,you can see the structure onnx models in https://lutzroeder.github.io/netron/.

Windows
Network Classes Input resolution Batch size Iterations CPU Running time GPU Running time TRT Running time*
MobileNet 1000 224x224 1 1000 19.56s 4.15s 1.05s
ERFNet 4 640x480 1 1000 >100s 12.93s 5.6s
Tiny_YOLOv2 20 416x416 1 1000 40.64s 2.97s 1.92s
Super Resolution with sub-pixel CNN - 224x224 1 1000 34.14s 1.79s 1.14s
Fast Neural Style Transfer - 224x224 1 1000 87.99s 4.64s -
Ubuntu
Network Classes Input resolution Batch size Iterations CPU Running time GPU Running time TRT Running time*
MobileNet 1000 224x224 1 1000 20.09s 4.24s 0.79s
ERFNet 4 640x480 1 1000 >100s 13.56s 4.90s

*The TensorRT engine is compiled with FP16 settings. Just add "trt_builder->setFp16Mode(true);" to 339 line of tensorrt_execution_provider.cc, if you build libonnxruntime yourself.

**This experiment is implemented on NVIDIA 2080Ti.

Classification


The onnx model is moblienet. You can download it in the link mentioned afore.

Segmentation


The onnx model is our trained erfnet. We use specific datasets to train erfnet.

Detection


The onnx model is Tiny YOLOv2.You can download it in the link mentioned afore.

Style transfer


The onnx model is Fast Neural Style Transfer. You can download it in the link mentioned afore.

Super resolution


The onnx model is Super Resolution with sub-pixel CNN. You can download it in the link mentioned afore.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].