onnx-simplifier
Paddle2ONNX
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| onnx-simplifier | Paddle2ONNX | |
|---|---|---|
| 3 | 1 | |
| 3,546 | 648 | |
| - | 4.5% | |
| 7.1 | 7.9 | |
| 16 days ago | 6 days ago | |
| C++ | Python | |
| Apache License 2.0 | Apache License 2.0 |
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onnx-simplifier
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Show: Cross-platform Image segmentation on video using eGUI, onnxruntime and ffmpeg
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.
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[Technical Article] OCR Upgrade
ONNX Simplifier：https://github.com/daquexian/onnx-simplifier
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PyTorch 1.10
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
Paddle2ONNX
What are some alternatives?
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
videocr-PaddleOCR - Extract hardcoded subtitles from videos using machine learning
torch2trt - An easy to use PyTorch to TensorRT converter
deepin-ocr
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)
PaddleOCR2Pytorch - PaddleOCR inference in PyTorch. Converted from [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
functorch - functorch is JAX-like composable function transforms for PyTorch.
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform
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, ... 🧠
opencv-mobile - The minimal opencv for Android, iOS, ARM Linux, Windows, Linux, MacOS, WebAssembly
TensorRT - PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT