Top 23 Python Pytorch Projects
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transformers
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
Latest mention: Confidence score prediction of huggingface transformers' models for extractive QA task | reddit.com/r/LanguageTechnology | 2020-12-27https://github.com/huggingface/transformers/blob/8217d4e37fce48490a68af7e8ce902af16318132/examples/question-answering/utils_qa.py#L151
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Real-Time-Voice-Cloning
Clone a voice in 5 seconds to generate arbitrary speech in real-time
Latest mention: I used AI tools to generate audio of SpongeBob rapping a portion of "Gangster's Paradise" | reddit.com/r/videos | 2021-01-05I've been struggling to get this https://github.com/CorentinJ/Real-Time-Voice-Cloning to work. But I always get stuck on mis-matching python libraries.
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pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
Latest mention: [P] Probabilistic Machine Learning: An Introduction, Kevin Murphy's 2021 e-textbook is out | reddit.com/r/MachineLearning | 2021-01-01 -
pytorch-CycleGAN-and-pix2pix
Image-to-Image Translation in PyTorch
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
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mmdetection
OpenMMLab Detection Toolbox and Benchmark
Latest mention: [D] How to organize deep learning projects on Github ? | reddit.com/r/MachineLearning | 2021-01-10You can check these repos: https://github.com/open-mmlab/mmdetection https://github.com/facebookresearch/detectron2
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pytorch-lightning
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.
PyTorchLightning/pytorch-lightning (Python): The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.
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fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
pytorch / fairseq
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EasyOCR
Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
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nni
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
microsoft / nni
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yolov5
YOLOv5 in PyTorch > ONNX > CoreML > TFLite
Yes, it is trained on COCO. All the codes are from this repo https://github.com/ultralytics/yolov5
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Bringing-Old-Photos-Back-to-Life
Bringing Old Photo Back to Life (CVPR 2020 oral)
microsoft/Bringing-Old-Photos-Back-to-Life (Python): Bringing Old Photo Back to Life (CVPR 2020 oral)
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datasets
🤗 The largest hub of ready-to-use NLP datasets for ML models with fast, easy-to-use and efficient data manipulation tools
Latest mention: [P] 611 text datasets in 467 languages in the new v1.2 release of HuggingFace datasets library | reddit.com/r/MachineLearning | 2021-01-05There will be 13 more bytthe end of this week, from Microsoft CodeXGlue, I had not the time to fix my PR earlier : https://github.com/huggingface/datasets/pull/997 .
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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.
Yes. But that's missing the point. Almost everything in the space uses a framework other than Core ML. Therefore most people need good development support for PyTorch/Tensorflow etc., not Core ML. The fact that Apple has a tool to import/convert models is nice, but not relevant. Also, there is onnx as an exchange format between the frameworks, and tools like MMdnn and others.
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stanza
Official Stanford NLP Python Library for Many Human Languages
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kornia
Open Source Differentiable Computer Vision Library for PyTorch
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espnet
End-to-End Speech Processing Toolkit
I really like google text to speech and use it for my own custom audiobooks, I've tried google's microsoft's, IBM's, and a few other research ones. IBM's sounds slightly better but has a much more restricted free monthly tier, google's and microsoft's has 1 million free characters per month which goes pretty far.
Like others are saying, it's slightly robotic but I've started to listen a ton by TTS and you definitely get used to it (you even start to hear inflection in it, which is cool). I use android smart audiobook app and you can control the sound levels, turning down the high pitch aspects also helps to make it easier to listen to for longer periods of time
For HN folks, there are some pretty reasonable research projects, especially by nvidia (glownet) which you can run yourself. They sound relatively similar but the training voices are much more restricted and not as good. If anyone knows of a github/etc with a nicer diy TTS I'd be interested. The best I've seen which is customizable is https://github.com/espnet/espnet but I had trouble getting it to work, then getting it to sound ok
(For anyone else going DIY I'll warn you that the failure modes for TTS is some unerring frankly creepy sounds. Google's TTS fails very well, even for strange words, and when it gets very confused it spells it out. Some of the research ones go into haunting unrelated syllables, sometimes repeating for 10s of seconds
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mindsdb
Predictive AI layer for existing databases.
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spotlight
Deep recommender models using PyTorch.
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hummingbird
Hummingbird compiles trained ML models into tensor computation for faster inference.
Latest mention: I learned about Microsoft's Hummingbird library today. 1000x performance?? | dev.to | 2020-09-23I took their sample code from Github and tweaked it to spit out times for each model's prediction, as well as increase the number of rows to 5 million. I used Google's Colab and selected GPU for my hardware accelerator. This gives an option to run code on GPU, not that all computations will happen on the GPU.
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coremltools
Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
And did you even know that Apple provides a CoreML conversion toolkit for converting models from PyTorch, TF, SKLearn and Keras to CoreML? You can comfortably train in your preferred tool and then convert the models to CoreML. (https://github.com/apple/coremltools). It currently supports a lot of conversions
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jina
An easier way to build neural search in the cloud
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PyTorch-NLP
Basic Utilities for PyTorch Natural Language Processing (NLP)
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pytorch-optimizer
torch-optimizer -- collection of optimizers for Pytorch
Latest mention: [R] AdasOptimizer Update: Cifar-100+MobileNetV2 Adas generalizes with Adas 15% better and 9x faster than Adam | reddit.com/r/MachineLearning | 2021-01-15
Index
What are some of the best open-source Pytorch projects in Python? This list will help you:
Project | Stars | |
---|---|---|
1 | transformers | 39,321 |
2 | Real-Time-Voice-Cloning | 22,118 |
3 | pytorch-tutorial | 19,371 |
4 | pytorch-CycleGAN-and-pix2pix | 14,037 |
5 | mmdetection | 13,335 |
6 | pytorch-lightning | 11,417 |
7 | fairseq | 10,980 |
8 | EasyOCR | 9,975 |
9 | nni | 8,684 |
10 | yolov5 | 7,781 |
11 | Bringing-Old-Photos-Back-to-Life | 7,293 |
12 | datasets | 6,482 |
13 | MMdnn | 5,166 |
14 | stanza | 5,091 |
15 | kornia | 3,509 |
16 | espnet | 3,308 |
17 | mindsdb | 3,146 |
18 | spotlight | 2,386 |
19 | hummingbird | 2,150 |
20 | coremltools | 2,079 |
21 | jina | 1,852 |
22 | PyTorch-NLP | 1,849 |
23 | pytorch-optimizer | 1,623 |