​​Clothing Dataset: Call for Action

Help to collect a public-domain dataset with images of clothes

Medium post: https://medium.com/data-science-insider/clothing-dataset-call-for-action-3cad023246c1

#dataset #clothing #cv #calltoarms

Ukrainian Open STT 1000 Hours

Following the path of Open STT in Russian, now you can enjoy a similar dataset in Ukrainian:

- Torrent Link
- GitHub Link

Congratulations to our Ukrainian friends for finally publishing a diverse easily downloadable dataset!

Their pages / dataset UX is still a bit rough on the edges, but compared how fast for example Common Voice accumulates data (130 hours for Russian and 43 hours for Ukrainian), UA Open STT and Open STT remain the best resource for respective languages to date.

Also unlike the majority of STT datasets which are (i) behind a paywall or sponsored by corporations (ii) have limited scope / domains (iii) fit some sort of agenda (i.e. use more GPUs than necessary, use our bloated tools, etc), this dataset is legit made by real people.

Also recently corporations have taken up the trend of rehashing publicly available data, which is cool, but unique data is still nowhere to be seen for obvious reasons (except for Common Voice, which is decent only for English).

#dataset

​​Counting Happiness and Where it Comes From

Researches asked 10 000 Mechanical Turk participants to name 10 things which are making them happy, resulting in creation of HappyDB.

And since that DB is open, Nathan Yau analyzed and vizualized this database in the perspective of subjects and actions, producing intersting visualization.

Hope that daily reading @opendatascience makes you at least content, if not happy.

Happines reason visualization link: https://flowingdata.com/2021/07/29/counting-happiness
HappyDB link: https://megagon.ai/projects/happydb-a-happiness-database-of-100000-happy-moments/

#dataset #emotions #visualization

🦜 Hi!

We are the first Telegram Data Science channel.

Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.


Ultimate Posts

* Where to start learning more about Data Science. https://github.com/open-data-science/ultimate_posts/tree/master/where_to_start
* @opendatascience channel audience research. https://github.com/open-data-science/ods_channel_stats_eda


Open Data Science

ODS.ai is an international community of people anyhow related to Data Science.

Website: https://ods.ai



Hashtags

Through the years we accumulated a big collection of materials, most of them accompanied by hashtags.

#deeplearning #DL — post about deep neural networks (> 1 layer)
#cv — posts related to Computer Vision. Pictures and videos
#nlp #nlu — Natural Language Processing and Natural Language Understanding. Texts and sequences
#audiolearning #speechrecognition — related to audio information processing
#ar — augmeneted reality related content
#rl — Reinforcement Learning (agents, bots and neural networks capable of playing games)
#gan #generation #generatinveart #neuralart — about neural artt and image generation
#transformer #vqgan #vae #bert #clip #StyleGAN2 #Unet #resnet #keras #Pytorch #GPT3 #GPT2 — related to special architectures or frameworks
#coding #CS — content related to software engineering sphere
#OpenAI #microsoft #Github #DeepMind #Yandex #Google #Facebook #huggingface — hashtags related to certain companies
#productionml #sota #recommendation #embeddings #selfdriving #dataset #opensource #analytics #statistics #attention #machine #translation #visualization


Chats

- Data Science Chat https://yangx.top/datascience_chat
- ODS Slack through invite form at website

ODS resources

* Main website: https://ods.ai
* ODS Community Telegram Channel (in Russian): @ods_ru
* ML trainings Telegram Channel: @mltrainings
* ODS Community Twitter: https://twitter.com/ods_ai

Feedback and Contacts

You are welcome to reach administration through telegram bot: @opendatasciencebot

🗿 StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis

Github: https://github.com/facebookresearch/StyleNeRF

Video: http://jiataogu.me/style_nerf

Paper: https://arxiv.org/abs/2110.08985

Project: http://jiataogu.me/style_nerf/

Dataset: https://github.com/facebookresearch/StyleNeRF#dataset

@ai_machinelearning_big_data

​​Segment Anything

The Segment Anything project aims to democratize image segmentation in computer vision, a core task used across various applications such as scientific imagery analysis and photo editing. Traditionally, accurate segmentation models require specialized expertise, AI training infrastructure, and large amounts of annotated data. This project introduces a new task, dataset, and model for image segmentation to overcome these challenges and make segmentation more accessible.

The researchers are releasing the Segment Anything Model (SAM) and the Segment Anything 1-Billion mask dataset (SA-1B), the largest segmentation dataset to date. These resources will enable a wide range of applications and further research into foundational models for computer vision. The SA-1B dataset is available for research purposes, while the SAM is provided under the permissive Apache 2.0 open license. Users can explore the demo to try SAM with their own images.

Paper link: https://arxiv.org/abs/2304.02643

Code link: https://github.com/facebookresearch/segment-anything

Demo link: https://segment-anything.com/demo

Blogpost link: https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/

Dataset link: https://ai.facebook.com/datasets/segment-anything/

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-sam

#deeplearning #cv #pytorch #imagesegmentation #dataset