ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations

Latest segmentation and detection approaches (DeepLabV3+, FasterRCNN) applied to street fashion images. Arxiv paper contains information about both: net and dataset.

Arxiv link: https://arxiv.org/abs/1807.01394
Paperdoll dataset: http://vision.is.tohoku.ac.jp/~kyamagu/research/paperdoll/

#segmentation #dataset #fashion #sv

Hey, our fellow colleagues at OpenDataScience community are labeling a meme dataset. You can help them with the markup just by viewing memes in this bot: @MemezoidBot

#DataSet #labeling

27.23TB of research data in torrents! Includes dataset such as:
- Breast Cancer Cell Segmentation
- Liver Tumor Segmentation
- MRI Lesion Segmentation in Multiple Sclerosis
- Electron Microscopy, Hippocampus
- Digital Surface & Digital Terrain Model

And courses recordings, including:
- Introduction to Computer Science [CS50x] [Harvard] [2018]
- Artificial Intelligence(EDX)
- Richard Feynman's Lectures on Physics (The Messenger Lectures) (🔥)
- [Coursera] Machine Learning (Stanford University) (ml)
- [Coursera] Natural Language Processing (Stanford University) (nlp)
- [Coursera] Neural Networks for Machine Learning (University of Toronto) (neuralnets)

http://academictorrents.com/

#course #torrent #dataset

#Google introduced Conceptual Captions, a new dataset and challenge for image captioning consisting of ~3.3 million image/caption pairs for the machine learning community to train and evaluate their own image captioning models.

Link: https://ai.googleblog.com/2018/09/conceptual-captions-new-dataset-and.html

#dataset

And #Google also launched #DataSet search. This is a huge breakthrough for the DS community, because now it will be easier to access some interesting data.

https://toolbox.google.com/datasetsearch

​​Google announced the updated YouTube-8M dataset

Updated set now includes a subset with verified 5-s segment level labels, along with the 3rd Large-Scale Video Understanding Challenge and Workshop at #ICCV19.

Link: https://ai.googleblog.com/2019/06/announcing-youtube-8m-segments-dataset.html

#Google #YouTube #CV #DL #Video #dataset

​​New dataset with adversarial examples

Natural Adversarial Examples are real-world and unmodified examples which cause classifiers to be consistently confused. The new dataset has 7,500 images, which we personally labeled over several months.

ArXiV: https://arxiv.org/abs/1907.07174
Dataset and code: https://github.com/hendrycks/natural-adv-examples

#Dataset #Adversarial

​​The Open Images Dataset V4 by GoogleAI

#GoogleAI present #OpenImagesV4, a #dataset of 9.2M images with unified annotations for:
– image #classification
– object #detection
– visual relationship detection

30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes

paper: https://arxiv.org/abs/1811.00982v2

​​Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study

Authors use the NER task to analyze the generalization behavior of existing models from different perspectives. Experiments with in-depth analyses diagnose the bottleneck of existing neural NER models in terms of breakdown performance analysis, annotation errors, dataset bias, and category relationships, which suggest directions for improvement.

The authors also release two datasets for future research: ReCoNLL and PLONER.

The main findings of the paper:
– the performance of existing models (including the state-of-the-art model) heavily influenced by the degree to which test entities have been seen in the training set with the same label
– the proposed measure enables to detect human annotation errors.

Once these errors are fixed, previous models can achieve new state-of-the-art results
– authors introduce two measures to characterize the data bias and the cross-dataset generalization experiment shows that the performance of NER systems is influenced not only by whether the test entity has been seen in the training set but also by whether the context of the test entity has been observed
– providing more training samples is not a guarantee of better results. A targeted increase in training samples will make it more profitable
– the relationship between entity categories influences the difficulty of model learning, which leads to some hard test samples that are difficult to solve using common learning methods


Paper: https://arxiv.org/abs/2001.03844
Github: https://github.com/pfliu-nlp/Named-Entity-Recognition-NER-Papers
Website: http://pfliu.com/InterpretNER/

#nlp #generalization #NER #annotations #dataset

​​MLSUM: The Multilingual Summarization Corpus

The first large-scale MultiLingual SUMmarization dataset, comprising over 1.5M article/summary pairs in French, German, Russian, Spanish, and Turkish. Its complementary nature to the CNN/DM summarization dataset for English.

For each language, they selected an online newspaper from 2010 to 2019 which met the following requirements:
0 being a generalist newspaper: ensuring that a broad range of topics is represented for each language allows minimizing the risk of training topic-specific models, a fact which would hinder comparative cross-lingual analyses of the models.
1 having a large number of articles in their public online archive.
2 Providing human written highlights/summaries for the articles that can be extracted from the HTML code of the web page.

Also, in this paper, you can remember about similar other datasets

paper: https://arxiv.org/abs/2004.14900
github: https://github.com/recitalAI/MLSUM
Instructions and code will soon.

#nlp #corpus #dataset #multilingual

​​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://t.me/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