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
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
#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
#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
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
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
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
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
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
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
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
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
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
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