Caffe2onnx Alternatives

Convert Caffe models to ONNX.
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Alternatives To asiryan/caffe2onnx
Project Name Stars Downloads Repos Using This Packages Using This Most Recent Commit Total Releases Latest Release Open Issues License Language
lutzroeder/netron 25,287 4 70 about 2 years ago 610 December 09, 2023 27 mit JavaScript
Visualizer for neural network, deep learning and machine learning models
Tencent/ncnn 18,693 0 1 about 2 years ago 26 October 27, 2023 1,010 other C++
ncnn is a high-performance neural network inference framework optimized for the mobile platform
ufoym/deepo 6,312 0 0 about 3 years ago 0 1 mit Python
Setup and customize deep learning environment in seconds.
microsoft/MMdnn 5,767 3 0 over 2 years ago 10 July 24, 2020 333 mit Python
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.
PaddlePaddle/VisualDL 4,652 0 4 over 2 years ago 42 August 23, 2021 143 apache-2.0 HTML
Deep Learning Visualization Toolkit(『飞桨』深度学习可视化工具 )
ysh329/deep-learning-model-convertor 3,197 0 0 almost 3 years ago 0 2
The convertor/conversion of deep learning models for different deep learning frameworks/softwares.
PINTO0309/PINTO_model_zoo 3,121 0 0 about 2 years ago 0 11 mit Python
A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
onnx/tutorials 3,094 0 0 over 2 years ago 0 108 apache-2.0 Jupyter Notebook
Tutorials for creating and using ONNX models
rwightman/gen-efficientnet-pytorch 1,353 0 2 almost 5 years ago 10 July 08, 2021 0 apache-2.0 Python
Pretrained EfficientNet, EfficientNet-Lite, MixNet, MobileNetV3 / V2, MNASNet A1 and B1, FBNet, Single-Path NAS
NervanaSystems/ngraph 1,310 0 1 over 5 years ago 9 November 09, 2019 0 apache-2.0 C++
nGraph - open source C++ library, compiler and runtime for Deep Learning
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