Omkar2118/datasets huggingface dataset:
S-SOD
Full name: Surveillance Salient Object Detection

To validate the generalization abilities of SOD models, we create a small-scale dataset by collecting the most challenging images with varying brightness and contrast, background and foreground colors overlap, among many others. We conclude that the current models, including ours, are not trust-worthy for real-world practice, demanding extensive future research for more efficient and generalized SOD models.

Introducing paper: Densely Deformable Efficient Salient Object Detection Network
Reddit Corpus

**Reddit Corpus** is part of a repository of conversational datasets consisting of hundreds of millions of examples, and a standardised evaluation procedure for conversational response selection models using '1-of-100 accuracy'. The Reddit Corpus contains 726 million multi-turn dialogues from the Reddit board. 

Source: [conversational-datasets](https://github.com/PolyAI-LDN/conversational-datasets)

Image source: [Henderson et al.](ttps://arxiv.org/pdf/1904.06472.pdf)

Introducing paper: A Repository of Conversational Datasets
Advising Corpus

Advising Corpus is a dataset based on an entirely new collection of dialogues in which university students are being advised which classes to take. These were collected at the University of Michigan with IRB approval. They were released as part of DSTC 7 track 1 and used again in DSTC 8 track 2.

Source: [Gunasekara et al.](http://workshop.colips.org/dstc7/papers/dstc7_task1_final_report.pdf)

Image source: [Gunasekara et al.](http://workshop.colips.org/dstc7/papers/dstc7_task1_final_report.pdf)

Introducing paper: DSTC7 Task 1: Noetic End-to-End Response Selection
French Wikipedia
Full name: French Wikipedia

**French Wikipedia** is a dataset used for pretraining the CamemBERT French language model. It uses the official 2019 French Wikipedia dumps

Introducing paper: CamemBERT: a Tasty French Language Model
CEDAR Signature

CEDAR Signature is a database of off-line signatures for signature verification. Each of 55 individuals contributed 24 signatures thereby creating 1,320 genuine signatures. Some were asked to forge three other writers’ signatures, eight times
per subject, thus creating 1,320 forgeries. Each signature was scanned at 300 dpi gray-scale and binarized using a gray-scale histogram. Salt pepper noise removal and slant normalization were two steps involved in image preprocessing. The database has 24 genuines and 24 forgeries available for each writer.

Introducing paper: Machine learning for signature verification
BanglaLekhaImageCaptions

This dataset consists of images and annotations in Bengali. The images are human annotated in Bengali by two adult native Bengali speakers. All popular image captioning datasets have a predominant western cultural bias with the annotations done in English. Using such datasets to train an image captioning system assumes that a good English to target language translation system exists and that the original dataset had elements of the target culture. Both these assumptions are false, leading to the need of a culturally relevant dataset in Bengali, to generate appropriate image captions of images relevant to the Bangladeshi and wider subcontinental context. The dataset presented consists of 9,154 images.

Introducing paper: Chittron: An Automatic Bangla Image Captioning System
Multi-Domain Sentiment

The Multi-Domain Sentiment Dataset contains product reviews taken from Amazon.com from many product types (domains). Some domains (books and dvds) have hundreds of thousands of reviews. Others (musical instruments) have only a few hundred. Reviews contain star ratings (1 to 5 stars) that can be converted into binary labels if needed.

Source: [Multi-Domain Sentiment Dataset (version 2.0)](https://www.cs.jhu.edu/~mdredze/datasets/sentiment/)

Introducing paper: Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
PAQ
Full name: Probably Asked Questions

**Probably Asked Questions** (**PAQ**) is a very large resource of 65M automatically-generated QA-pairs. PAQ is a semi-structured Knowledge Base (KB) of 65M natural language QA-pairs, which models can memorise and/or learn to retrieve from. PAQ differs from traditional KBs in that questions and answers are stored in natural language, and that questions are generated such that they are likely to appear in ODQA datasets. PAQ is automatically constructed using a question generation model and Wikipedia.

Source: [Lewis et al.](https://arxiv.org/pdf/2102.07033.pdf)

Image source: [Lewis et al.](https://arxiv.org/pdf/2102.07033.pdf)

Introducing paper: PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
BABEL Project
Full name: BABEL

**BABEL** is a multilingual corpus of conversational telephone speech from IARPA, which includes Asian and African languages.
AIDA CoNLL-YAGO

**AIDA CoNLL-YAGO** contains assignments of entities to the mentions of named entities annotated for the original [CoNLL 2003 entity recognition task](https://www.clips.uantwerpen.be/conll2003/ner/). The entities are identified by YAGO2 entity name, by Wikipedia URL, or by Freebase mid. 

Source: [AIDA CoNLL-YAGO](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/ambiverse-nlu/aida/downloads)

Introducing paper: Robust Disambiguation of Named Entities in Text
BTFDBB
Full name: BTF Database Bonn

Reflectance measurements of Bidirectional Texture Functions (BTFs)

Database contains both flat samples:

![](https://cg.cs.uni-bonn.de/typo3temp/pics/T_d3f3eb3fec.png)
![](https://cg.cs.uni-bonn.de/typo3temp/pics/L_0446825446.png)
![](https://cg.cs.uni-bonn.de/typo3temp/pics/S_1eb36192f6.png)

as well as 3D geometry with texture mapped BTFs:

![](https://cg.cs.uni-bonn.de/typo3temp/pics/g_6638002797.jpg)
![](https://cg.cs.uni-bonn.de/typo3temp/pics/O_0f9dce16bf.jpg)

furthermore, there are some multispectral BTFs:

![](https://cg.cs.uni-bonn.de/typo3temp/pics/p_cc0a283544.jpg)
ReVerb45K
Full name: ReVerb45K

Open KB canonicalization dataset.
ReVerb45K increases the entity number to 7.5K and has 45K triples in total. ReVerb45K extract a source sentence for each triple from ClueWeb09
UBOFAB19
Full name: SVBRDF Database Bonn

A database of several hundred high quality fabric material measurements, provided as carefully calibrated rectified HDR images, together with SVBRDF fits.

![](https://cg.cs.uni-bonn.de/uploads/svbrdfs/UBOFAB19/train/img/mat0061pv.png)
![](https://cg.cs.uni-bonn.de/uploads/svbrdfs/UBOFAB19/train/img/mat0025pv.png)
![](https://cg.cs.uni-bonn.de/uploads/svbrdfs/UBOFAB19/val/img/mat0047pv.png)
![](https://cg.cs.uni-bonn.de/uploads/svbrdfs/UBOFAB19/val/img/mat0056pv.png)
![](https://cg.cs.uni-bonn.de/uploads/svbrdfs/UBOFAB19/val/img/mat0058pv.png)
![](https://cg.cs.uni-bonn.de/uploads/svbrdfs/UBOFAB19/val/img/mat0059pv.png)
![](https://cg.cs.uni-bonn.de/uploads/svbrdfs/UBOFAB19/train/img/mat0054pv.png)

Measurement HDR images are provided in OpenEXR format

![](https://cg.cs.uni-bonn.de/uploads/svbrdfs/UBOFAB19/val/html/img/mat0047_inputs.png)

SVBRDF fits are provided in X-Rite AxF format

![](https://cg.cs.uni-bonn.de/uploads/svbrdfs/UBOFAB19/val/html/img/mat0047_labels.png)

Further geometric and radiometric calibration data is available as well.

Introducing paper: Learned Fitting of Spatially Varying BRDFs
APPBENCH
Full name: SVBRDF Database Bonn

A database of 56 high quality fabric material measurements, provided as carefully calibrated rectified HDR images, together with SVBRDF fits. Used in the [Fabric Appearance Challange](https://competitions.codalab.org/competitions/24979).

![](https://cg.cs.uni-bonn.de/uploads/svbrdfs/APPBENCH/html/videos/mat0386.webp)
![](https://cg.cs.uni-bonn.de/uploads/svbrdfs/APPBENCH/html/videos/mat0396.webp)

![](https://cg.cs.uni-bonn.de/uploads/svbrdfs/APPBENCH/html/videos/mat0413.webp)
![](https://cg.cs.uni-bonn.de/uploads/svbrdfs/APPBENCH/html/videos/mat0410.webp)

Measurement HDR images are provided in OpenEXR format

![](https://cg.cs.uni-bonn.de/uploads/svbrdfs/APPBENCH/html/img/mat0386_inputs.png)

SVBRDF fits are provided in X-Rite AxF format

![](https://cg.cs.uni-bonn.de/uploads/svbrdfs/APPBENCH/html/img/mat0386_labels.png)

Further geometric and radiometric calibration data is available as well.

Introducing paper: Fabric Appearance Benchmark
Chickenpox Cases in Hungary

**Chickenpox Cases in Hungary** is a spatio-temporal dataset of weekly chickenpox (childhood disease) cases from Hungary. It can be used as a longitudinal dataset for benchmarking the predictive performance of spatiotemporal graph neural network architectures. The dataset consists of a county-level adjacency matrix and time series of the county-level reported cases between 2005 and 2015. There are 2 specific related tasks:

- County level case count prediction.
- National level case count prediction.

Introducing paper: Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
Multimodal Opinionlevel Sentiment Intensity
Full name: MOSI

Multimodal Opinionlevel Sentiment Intensity (MOSI) contains: (1) multimodal observations including transcribed speech and visual gestures as well as automatic audio and visual features, (2) opinion-level subjectivity segmentation, (3) sentiment intensity annotations with high coder agreement, and (4) alignment between words, visual and acoustic features.

Source: [Zadeh et al](https://arxiv.org/pdf/1606.06259.pdf)

Image source: [Zadeh et al](https://arxiv.org/pdf/1606.06259.pdf)

Introducing paper: MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos