ncnn VS onnx-simplifier

Compare ncnn vs onnx-simplifier and see what are their differences.

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ncnn onnx-simplifier
12 3
19,234 3,550
2.1% -
9.4 6.5
2 days ago 19 days ago
C++ C++
GNU General Public License v3.0 or later Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

ncnn

Posts with mentions or reviews of ncnn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-02-12.

onnx-simplifier

Posts with mentions or reviews of onnx-simplifier. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-11-20.
  • Show: Cross-platform Image segmentation on video using eGUI, onnxruntime and ffmpeg
    2 projects | /r/rust | 20 Nov 2022
    onnx-simplifier can shed some of incompatibilities in widespread use, but is itself bug ridden and lagging behind the standard. For any serious model, or when you don't get lucky simplifying the model upstream, you'd generally want good support of opset 11.
  • [Technical Article] OCR Upgrade
    8 projects | /r/deepin | 12 Jun 2022
    ONNX Simplifier:https://github.com/daquexian/onnx-simplifier
  • PyTorch 1.10
    8 projects | news.ycombinator.com | 22 Oct 2021
    As far as I know, the ONNX format won't give you a performance boost on its own. However, there are ONNX optimizers for the ONNX runtime which will speed up your inference.

    But if you are using Nvidia Hardware, then TensorRT should give you the best performance possible, especially if you change the precision level. Don't forget to simplify your ONNX model before you converting it to TensorRT though: https://github.com/daquexian/onnx-simplifier

What are some alternatives?

When comparing ncnn and onnx-simplifier you can also consider the following projects:

XNNPACK - High-efficiency floating-point neural network inference operators for mobile, server, and Web

onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

rife-ncnn-vulkan - RIFE, Real-Time Intermediate Flow Estimation for Video Frame Interpolation implemented with ncnn library

torch2trt - An easy to use PyTorch to TensorRT converter

deepdetect - Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE

PaddleOCR - Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)

netron - Visualizer for neural network, deep learning and machine learning models

functorch - functorch is JAX-like composable function transforms for PyTorch.

darknet - Convolutional Neural Networks

nn - 🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

RPi_64-bit_Zero-2-image - Raspberry Pi Zero 2 W 64-bit OS image with OpenCV, TensorFlow Lite and ncnn Framework.

TensorRT - PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT