Transformer-SSL VS byol-pytorch

Compare Transformer-SSL vs byol-pytorch and see what are their differences.

Transformer-SSL

This is an official implementation for "Self-Supervised Learning with Swin Transformers". (by SwinTransformer)

byol-pytorch

Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch (by lucidrains)
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Transformer-SSL byol-pytorch
2 1
639 1,790
2.3% 0.5%
0.0 4.2
almost 4 years ago 7 months ago
Python Python
MIT License MIT License
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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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Transformer-SSL

Posts with mentions or reviews of Transformer-SSL. We have used some of these posts to build our list of alternatives and similar projects.

byol-pytorch

Posts with mentions or reviews of byol-pytorch. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing Transformer-SSL and byol-pytorch you can also consider the following projects:

dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

lightly - A python library for self-supervised learning on images.

simsiam-cifar10 - Code to train the SimSiam model on cifar10 using PyTorch

pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

Ne2Ne-Image-Denoising - Deep Unsupervised Image Denoising, based on Neighbour2Neighbour training

Unsupervised-Classification - SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]

PaddleSpeech - Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.

animessl - Train vision models with vissl + illustrated images

spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python

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Bad PDFs = bad UX. Slow load times, broken annotations, clunky UX frustrates users. Nutrient’s PDF SDKs gives seamless document experiences, fast rendering, annotations, real-time collaboration, 100+ features. Used by 10K+ devs, serving ~half a billion users worldwide. Explore the SDK for free.
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