Omkar2118/datasets huggingface dataset:
ASSET
Full name: ASSET

ASSET is a new dataset for assessing sentence simplification in English. ASSET is a crowdsourced multi-reference corpus where each simplification was produced by executing several rewriting transformations.

Source: [ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations](https://arxiv.org/pdf/2005.00481v1.pdf)

Introducing paper: ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations
TurkCorpus

TurkCorpus, a dataset with 2,359 original sentences from English Wikipedia, each with 8 manual reference simplifications.
The dataset is divided into two subsets: 2,000 sentences for validation and 359 for testing of sentence simplification models.

Introducing paper: Optimizing Statistical Machine Translation for Text Simplification
Chest X-Ray Images (Pneumonia)
Full name: Abdul Manaf

The normal chest X-ray (left panel) depicts clear lungs without any areas of abnormal opacification in the image. Bacterial pneumonia (middle) typically exhibits a focal lobar consolidation, in this case in the right upper lobe (white arrows), whereas viral pneumonia (right) manifests with a more diffuse ‘‘interstitial’’ pattern in both lungs.
http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5
IRMA
Full name: 15,363 IRMA images of 193 categories for ImageCLEFmed 2009

This collection compiles anonymous radiographs, which have been arbitrarly selected from routine at the Department of Diagnostic Radiology, Aachen University of Technology (RWTH), Aachen, Germany. The imagery represents different ages, genders, view positions and pathologies. Therefore, image quality varies significantly. All images were downscaled to fit into a 512 x 512 bounding box maintaining the original aspect ratio. All images were classified according to the IRMA code. Based on this code, 193 categories were defined. For 12,677 images, these categories are provided. The remaining 1,733 images without code are used as test data for the ImageCLEFmed 2009 competition.

-    Training data: 12,677 radiographs with known categories
-    Class distribution: Distribution of classes in the training data.
-    Test data: 1,733 radiographs without classification.
MLFP
Full name: Multispectral Latex Mask based Video Face Presentation Attack

The **MLFP** dataset consists of face presentation attacks captured with seven 3D latex masks and three 2D print attacks. The dataset contains videos captured from color, thermal and infrared channels.

Source: [Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks](https://arxiv.org/abs/2007.11457)
Image Source: [http://iab-rubric.org/papers/2017_cvprw_18.pdf](http://iab-rubric.org/papers/2017_cvprw_18.pdf)

Introducing paper: Face Presentation Attack with Latex Masks in Multispectral Videos
CoNLL++

CoNLL++ is a corrected version of the CoNLL03 NER dataset where 5.38% of the test sentences have been fixed.

Source: [CrossWeigh: Training Named Entity Tagger from Imperfect Annotations](/paper/crossweigh-training-named-entity-tagger-from)

Introducing paper: CrossWeigh: Training Named Entity Tagger from Imperfect Annotations
ViSal

DataViSal.rar (including the ground truth data) is our new collected dataset for the following paper.

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W. Wang, J. Shen, and L. Shao, 
Consistent video saliency using local gradient flow optimization and global refinement,  
IEEE Trans. on Image Processing, 24(11):4185-4196, 2015  
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The related source code can be downloaded from:
https://github.com/shenjianbing/videosal

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Note:

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The data and code files are free to use for research purposes. 
If you use them for research purposes,
you should cite above paper in any resulting publication.

This code also uses some publicly available functions.

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Contact Information

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Email:

    wenguanwang@bit.edu.cn

	shenjianbing@bit.edu.cn

	  shenjianbingcg@gmail.com
SOC
Full name: Salient Objects in Clutter

SOC (Salient Objects in Clutter) is a dataset for Salient Object Detection (SOD). It includes images with salient and non-salient objects from daily object categories. Beyond object category annotations, each salient image is accompanied by attributes that reflect common challenges in real-world scenes.

Introducing paper: Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground
CoSal2015
Full name: CoSal2015

Cosal2015 is a large-scale dataset for co-saliency detection which consists of 2,015 images of 50 categories, and each group suffers from various challenging factors such as complex environments, occlusion issues, target appearance variations and background clutters, etc. All these increase the difficulty for accurate co-saliency detection.

Source: [Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection](https://arxiv.org/abs/2003.06167)
Image Source: [https://arxiv.org/pdf/1604.07090.pdf](https://arxiv.org/pdf/1604.07090.pdf)

Introducing paper: Detection of Co-salient Objects by Looking Deep and Wide
SIP
Full name: Salient Person

The **Salient Person** dataset (**SIP**) contains 929 salient person samples with different poses and illumination conditions.

Source: [Accurate RGB-D Salient Object Detection via Collaborative Learning](https://arxiv.org/abs/2007.11782)
Image Source: [https://arxiv.org/pdf/1907.06781.pdf](https://arxiv.org/pdf/1907.06781.pdf)

Introducing paper: Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
NJU2K

**NJU2K** is a large RGB-D dataset containing 1,985 image pairs. The stereo images were collected from the Internet and 3D movies, while photographs were taken by a Fuji W3 camera.

Source: [Bifurcated Backbone Strategy for RGB-D Salient Object Detection](https://arxiv.org/abs/2007.02713)
Image Source: [Depth saliency based on anisotropic center-surround difference](https://doi.org/10.1109/ICIP.2014.7025222)

Introducing paper: Depth saliency based on anisotropic center-surround difference