Omkar2118/datasets huggingface dataset:
Graph dataset MOLT-4
Full name: MOLT-4

Dataset introduced by Xifeng Yan et al.

in: SIGMOD '08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data, June 2008, Pages 433–444, https://doi.org/10.1145/1376616.1376662

The dataset is now hosted by TUD.  
The dataset consists of small molecules activities against Leukemia tumors.

Introducing paper: TUDataset: A collection of benchmark datasets for learning with graphs
Graph dataset MCF-7
Full name: MCF-7

Dataset introduced by Xifeng Yan et al.

in:  SIGMOD '08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data, June 2008, Pages 433–444, https://doi.org/10.1145/1376616.1376662

The dataset is now hosted by TUD.  
The dataset consists of small molecules activities against breast cancer tumors.

Introducing paper: TUDataset: A collection of benchmark datasets for learning with graphs
IRV2V
Full name: IRregular V2V Dataset

To facilitate research on asynchrony for collaborative perception, we simulate the first collaborative perception dataset with different temporal asynchronies based on CARLA, named IRregular V2V(IRV2V). We set 100ms as ideal sampling time interval and simulate various asynchronies in real-world scenarios from two main aspects: i) considering that agents are unsynchronized with the unified global clock, we uniformly sample a time shift $\delta_s\sim \mathcal{U}(-50,50)\text{ms}$ for each agent in the same scene, and ii) considering the trigger noise of the sensors, we uniformly sample a time turbulence $\delta_d\sim \mathcal{U}(-10,10)\text{ms}$ for each sampling timestamp. The final asynchronous time interval between adjacent timestamps is the summation of the time shift and time turbulence. In experiments, we also sample the frame intervals to achieve large-scale and diverse asynchrony. 
Each scene includes multiple collaborative agents ranging from 2 to 5. Each agent is equipped with 4 cameras with the resolution 600 $\times$ 800 and a 32-channel LiDAR. The detection range is 281.6m $\times$ 80m. It results in 34K images and 8.5K LiDAR sweeps. See more details in the Appendix.
MMface4D

MMFace4D is a large-scale multi-modal 4D (3D sequence) face dataset consisting of 431 identities, 35,904 sequences, and 3.9 million frames
MMFace4D has three appealing characteristics: 1) highly diversified subjects and corpus, 2) synchronized audio and 3D mesh sequence with high-resolution face details, and 3) low storage cost with a new efficient compression algorithm on 3D mesh sequences.
IMCPT-SparseGM-50

IMCPT-SparseGM dataset is a new visual graph matching benchmark addressing partial matching and graphs with larger sizes, based on the novel stereo benchmark [Image Matching Challenge PhotoTourism  (IMC-PT)  2020](https://www.cs.ubc.ca/research/image-matching-challenge/2020/). This dataset is released in CVPR 2023 paper [*Deep Learning of Partial Graph Matching via Differentiable Top-K*](https://openreview.net/forum?id=4OoXQPGd1s).

| **# images** | **# classes** | **avg # nodes** | **avg # edges** | **# universe** | **partial rate** |
| ------------ | ------------- | --------------- | ----------- | -------------- | ---------------- |
| 25765        | 16            | 21.36           | 54.71       | 50             | 57.3%            |

Introducing paper: Deep Learning of Partial Graph Matching via Differentiable Top-K
IMCPT-SparseGM-100

IMCPT-SparseGM dataset is a new visual graph matching benchmark addressing partial matching and graphs with larger sizes, based on the novel stereo benchmark [Image Matching Challenge PhotoTourism  (IMC-PT)  2020](https://www.cs.ubc.ca/research/image-matching-challenge/2020/). This dataset is released in CVPR 2023 paper [*Deep Learning of Partial Graph Matching via Differentiable Top-K*](https://openreview.net/forum?id=4OoXQPGd1s).

| **# images** | **# classes** | **avg # nodes** | **avg # edges** | **# universe** | **partial rate** |
| ------------ | ------------- | --------------- | ----------- | -------------- | ---------------- |
| 25765        | 16            | 44.48           | 123.99      | 100            | 55.5%            |

Introducing paper: Deep Learning of Partial Graph Matching via Differentiable Top-K
MCCSD
Full name: Mandarin Chinese Cued Speech Dataset

This MCCS dataset is the first large-scale Mandarin Chinese Cued Speech dataset. This dataset covers 23 major categories of scenarios (e.g, communication, transportation and shoping) and 72 subcategories of scenarios (e.g, meeting, dating and introduction). It is recorded by four skilled native Mandarn Chinese Cued Speech cuers with portable cameras on the mobile phones. The Cued Speech videos are recorded with 30fps and 1280x720 format. We provide the raw Cued Speech videos, text file (with 1000 sentences) and corresponding annotations which contains two kind of data annotation. One is continuious video annotation with ELAN, the other is discrete audio annotations with Praat.
LDCT-and-Projection-data

LDCT-and-Projection-data  医学去噪数据集
Celeb-HQ Facial Identity Recognition Dataset
Full name: Celeb-HQ Facial Identity Recognition Dataset

Celeb-HQ Facial Identity Recognition Dataset

* This dataset is curated for the facial identity classification task.
* There are 307 identities (celebrities).
* Each identity has 15 or more images.
* The dataset contains 5,478 images.
* There are 4,263 training images.
* There are 1,215 test images.

Introducing paper: Unrestricted Black-box Adversarial Attack Using GAN with Limited Queries
Celeb-HQ Face Gender Recognition Dataset
Full name: Celeb-HQ Face Gender Recognition Dataset

Celeb-HQ Face Gender Recognition Dataset

* This dataset is curated for the face gender classification task.
* The dataset contains 30,000 images.
* There are 23,999 train images.
* There are 6,001 test images.
* The whole face images are divided into two classes.
* There are 11,057 male images.
* There are 18,943 female images.

Introducing paper: Unrestricted Black-box Adversarial Attack Using GAN with Limited Queries
LSA-T
Full name: Lengua de Señas Argentina - Traducción

LSA-T is the first continuous Argentinian Sign Language (LSA) dataset. It contains 14,880 sentence level videos of LSA extracted from the [CN Sordos YouTube channel](https://www.youtube.com/c/CNSORDOSARGENTINA) with labels and keypoints annotations for each signer. Videos are in 30 FPS full HD (1920x1080).

* [Download link](https://app.seni.ar/datasets/lsat.7z) (45GB compressed)
* [Visualization notebook](https://colab.research.google.com/drive/1kj5ztYw_57fi6wo2dpL18UkBR9ciV6ki)
* [Presentation paper](https://arxiv.org/pdf/2211.15481.pdf) (preprint PDF)

Introducing paper: LSA-T: The first continuous Argentinian Sign Language dataset for Sign Language Translation
MulRan
Full name: MulRan: Multimodal Range Dataset for Urban Place Recognition

MulRan is a dataset for Place Recognition and SLAM. The datasets were recorded in urban areas and contain sensor data of a car that is equipped with a 3D LiDAR (OS1-64) and a rotating radar (Navtech CIR204-H). For each sequence, the car is revisiting places several times.
RWTH-PHOENIX Handshapes dev set
Full name: RWTH-PHOENIX-Weather 2014 MS Handshapes dev set

We manually labelled 3359 images from the RWTH-PHOENIX-Weather 2014 Development set. 

Some of the 45 encountered pose-independent hand shape classes are depicted in Figure 1. They show the large intra-class variance and the strong similarity between several classes. The hand shapes occur with different frequency in the data. The distribution of counts per class can be verified in Figure 2 showing that the top 14 hand shapes explain 90% of the annotated samples.


For our works on hand shape recognition we follow the hand shape taxonomy by the danish sign language lexicon team (Jette H. Kristoffersen and Thomas Troelsgård, Center for Tegnsprog, Denmark http://www.tegnsprog.dk), which amounts to over 60 different hand shapes, often with very subtle differences such as a flexed versus straight thumb. The employed classes are shown in Table1.

Introducing paper: Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data Is Continuous and Weakly Labelled
mBBC dataset
Full name: Multilingual BBC news

To construct our multilingual dataset - mBBC - we gathered news articles from various BBC news websites in 43 different languages. This selection was based on the fact that BBC broadcasts news in these 43 languages, providing a global coverage across continents, and spanning a diverse range of language families, scripts, resource-levels, and word order ensuring a comprehensive representation of linguistic diversity.
We collected data from various language families such as Indo-European, Sino-Tibetan, Niger-Congo, Austronesian, Dravidian, and more, encompassing several scripts like Latin, Cyrillic, Arabic, Devanagari, Chinese characters, and others. This extensive representation facilitates a comprehensive evaluation of multilingual language models across different linguistic contexts. Moreover, the dataset includes both high-resource languages like English, Spanish, and French, benefiting from extensive linguistic resources, as well as low-resource languages such as Somali, Burmese, and Nepali, with limited resources or smaller speaker populations. Including languages with varying resource levels enables us to assess the adaptability and effectiveness of multilingual language models across diverse linguistic settings. To ensure an unbiased and robust analysis, our dataset consists of news articles of minimum text length of 500 characters, sourced from reputable sources in 2023, ensuring the models studied have not seen the data during training in the most new LLMs.

Introducing paper: Exploring the Maze of Multilingual Modeling
CodeInstruct
Full name: InstructCoder, CodeInstruct

InstructCoder is the first dataset designed to adapt LLMs for general code editing. It consists of over 100k instruction-input-output triplets and covers multiple distinct code editing scenarios, generated by ChatGPT. LLaMA-33B finetuned on InstructCoder performs on par with ChatGPT on a real-world test set derived from GitHub commits.

Introducing paper: CodeInstruct: Empowering Language Models to Edit Code
WU-Minn HCP Data - 1200 Subjects

This HCP data release includes high-resolution 3T MR scans from young healthy adult twins and non-twin siblings (ages 22-35) using four imaging modalities: structural images (T1w and T2w), resting-state fMRI (rfMRI), task-fMRI (tfMRI), and high angular resolution diffusion imaging (dMRI). Behavioral and other individual subject measure data (both NIH Toolbox and non-Toolbox measures) is available on all subjects. MEG data and 7T MR data is available for a subset of subjects (twin pairs). The Open Access Dataset includes imaging data and most behavioral data. To protect subject privacy, some of the data (e.g., which subjects are twins) are part of a Restricted Access dataset.
AjwaOrMedjool
Full name: AjwaOrMedjool: a binary balanced dataset to teach machine learning‏

The dataset contains three subsets:

1- a dataset containing hand-crafted features to classify two types of organic dates (Ajwa or Medjool);
2- a dataset containing tabular data with features created automatically using deep learning to classify the two organic date types (Ajwa or Medjool);
3- a dataset for images of Ajwa and Medjool. This study is considered as the first work in Arabic using shallow machine learning and deep learning to create accurate models for classifying organic Saudi dates, which would enable scholars, researchers, and developers to create machine learning applications for classifying Saudi dates in various forms like websites, mobile apps, microcontrollers, tiny machine learning and internet of things applications.

Please cite the following paper: Bati GF. Ajwa or Medjool: a binary balanced dataset to teach machine learning. Journal of Information Studies & Technology 2023:2.12. https://doi.org/10.5339/jist.2023.12

عجوة أو مجدول هي مجموعة بيانات متوازنة الصنفين لتصنيف التمور السعودية العضوية تتكون من ثلاث مجموعات فرعية: الأولى: تحوي البيانات المجدولة ذات الخصائص اليدوية لتصنيف التمور العضوية (عجوة أو مجدول)، والثانية: تجمع البيانات المجدولة ذات الخصائص المولدة أتوماتيكيّاً باستخدام التعلم العميق لتصنيف التمور العضوية (عجوة أو مجدول)، والثالثة: تجمع صوراً لتمور العجوة والمجدول. كما أنه أول بحث باللغة العربية يستخدم نماذج تعلم الآلة التقليدية والتعلم العميق لإنشاء نماذج ذات أداء عالٍ لتصنيف التمور السعودية العضوية بدون برمجة، مما يمكن الدارسين والباحثين والمطورين من تطوير تطبيقات تعلم آلة لتصنيف التمور السعودية بأشكال متنوعة في مواقع الإنترنت أو تطبيقات الجوالات أو في المتحكمات الدقيقة وتطبيقات إنترنت الأشياء وتعلم الآلات الصغيرة.

كرماً الاستشهاد بالبحث التالي عند استخدام مجموعة البيانات: Bati GF. Ajwa or Medjool: a binary balanced dataset to teach machine learning. Journal of Information Studies & Technology 2023:2.12. https://doi.org/10.5339/jist.2023.12

فيديوهات عربية تشرح مجموعة البيانات: https://youtu.be/bPYHOYo4_Tw?feature=shared&t=1418 https://youtu.be/ADOuweANc5I?feature=shared&t=5775 https://youtu.be/PThKbc1kTSM?feature=shared&t=3253

Introducing paper: Ajwa or Medjool: a binary balanced dataset to teach machine learning عجوة أو مجدول: مجموعة بيانات متوازنة الصنفين لتدريس تعلم الآلة‏
PDFVQA

PDFVQA: A New Dataset for Real-World VQA on PDF Documents

Introducing paper: PDFVQA: A New Dataset for Real-World VQA on PDF Documents