creaf

Deep Learning | Computer Vision

Co-creator of #albumentations.

[Vladimir Iglovikov @viglovikov]

New release of the best image augmentations library Albumentations - 1.2.0

- Four new transforms
- Improved key points support
- Improved documentation
- Various bug fixes

Stats:
- 280k downloads per month
- 10.4k stars at GitHub.

More details: https://t.co/sMQA92YkBp

[Vladimir Iglovikov @viglovikov]

Post about #albumentations #OpenSource library.

I talk about how it was born from @kaggle competitions, how it evolved, and how we promoted it among developers and scientists.

https://t.co/XVaY2PY0Zw

[creaf @creaf]

#Albumentations 1.1.0 is out!

The new release of a fast and flexible library for image augmentation includes new transformations and improvements for the current ones.

Thanks to @Dipetm @viglovikov @cvtalks @AlBuslaev and all our contributors.

https://t.co/NMwgwx6NpT

[Mikhail Druzhinin @Dipetm]

#Albumentations 1.0.0 has been released!
New version contains:
- 10 new transforms
- bug fixes
etc

See the release notes for details https://t.co/QLEfhmpYgk
Thanks a lot to core team
@viglovikov @creaf @cvtalks @AlBuslaev
and everyone who helps improve the library

[Evil Martians @evilmartians]

Fresh Martian Chronicles are out: a hands-on introduction to a Catalyst framework for #deeplearning by @gazay. Learn how to build your own pipeline for an image classifier and deploy a trained model to Heroku

“Beyond Fashion: Deep learning with Catalyst”: https://t.co/tTWEaBxZn2

[Sebastian Raschka @rasbt]

Another nice contribution to our "Machine Learning with Python" special issue just got published: "Albumentations: Fast and Flexible Image Augmentations" by @alxndrkalinin et al. Paper link: https://t.co/12hPieRCHC GitHub Repo: https://t.co/ExykgMDUHv

[Alex Kalinin @alxndrkalinin]

Our peer-reviewed open-access Albumentations paper has been published in the special issue Machine Learning in Python @InformationMDPI:
https://t.co/981XYi5MZa

It covers the design considerations, main features, performance, & adoption of Albumentations https://t.co/gd7EE20CoD

[Vladimir Iglovikov @viglovikov]

Now we have an official website for #Albumentations!

Check this out at https://t.co/qePOYImw2K

Big thanks to @creaf for making it real!

#DeepLearning #ArtificialIntelligence

[Одна інтеллектуальна одиниця @cvtalks]

A fresh release of pytorch-toolbelt 0.3 is out:
Changelog: https://t.co/CDevvorZiF

If you're unfamiliar - it's a python library with a lot of bells and whistles for PyTorch for fast R&D prototyping and Kaggle farming:
https://t.co/UTrLBV7co9

[Vladimir Iglovikov @viglovikov]

On my new year flight from Lima to SF, I wrote a blog post on the path from my previous job to the current one.

TL;DR => it was an ocean of pain and experience obtained at @kaggle was very useful 😀

If you like it => 50 claps.
If not => 49 😀

https://t.co/R42aq99RPc

[Vladimir Iglovikov @viglovikov]

I am thrilled that image Augmentation library #albumentations that was born out of @kaggle competitions by @AlBuslaev @creaf @cvtalks @Dipetm and @viglovikov became a part of the @PyTorch ecosystem!

https://t.co/oGokF8ZxeG

[Vladimir Iglovikov @viglovikov]

https://t.co/QpL6eX7Csq

My interview with the Data Futurology podcast.

[Sergey Kolesnikov @Scitator]

Catalyst, high-level utils for @PyTorch DL & RL research.
Release 19.05 thread

PS. 600 starts on Github - done 🚀

[Iaroslav Melekhov @iMelekhov]

We have finally released the source code and pre-trained models of our recent work "DGC-Net: Dense Geometric Correspondence network"🎉
Paper: https://t.co/kgLWwInKIh
Github: https://t.co/7JmkEy8x3T
Project page: https://t.co/ID1yaeJYOy
@tiulpin @mapo1 @PyTorch @CSAalto

[Sergey Kolesnikov @Scitator]

Hell yeah,
We have released catalyst 19.03 final version!

- tests, lots of new tests for train/infer pipeline validation
- registry refactoring for simplified customisation
- stablelized API
- minor improvements

https://t.co/oD7wAdPPup

[Vladimir Iglovikov @viglovikov]

In #albumentations v0.2.2 @AlBuslaev @creaf @cvtalks and @viglovikov added experimental support for weather transforms from the #automold library.

Let us know if they help or you have ideas on how to improve them.

[Sergey Kolesnikov @Scitator]

Are your interested in reproducible RL? Or want a competitive benchmark of current off-policy RL algorithms?
check out https://t.co/bMM5vPTHpB, catalyst.rl – framework for distributed RL training on top of @PyTorch
Various RL algorithms and auxiliary tricks included

[Sanyam Bhutani @bhutanisanyam1]

The Interview with @kaggle Grandmaster and Senior CV Engineer @LyftLevel5: Vladimir Iglovikov @viglovikov just got published @hackernoon. The Grandmaster has really been kind enough to share *ALL* of his secrets, you can find all of them here: https://t.co/OcPt1Ihbvo

[Kaggle @kaggle]

Bookmark this amazing library of image augmentations 😵 by #Kaggle Masters @AlBuslaev @creaf @viglovikov and @cvtalks | https://t.co/7hTShJvbqZ #resources #machinelearning

[Sergey Kolesnikov @Scitator]

Catalyst.dl - high-level utils for @Pytorch DL research v19.03
You get a training loop with metrics, early-stopping, model checkpointing and other features without the boilerplate.
Break the cycle - use the Catalyst!
https://t.co/oD7wAdPPup