Omkar2118/datasets huggingface dataset:
CATS
Full name: Color and Thermal Stereo Benchmark

A dataset consisting of stereo thermal, stereo color, and cross-modality image pairs with high accuracy ground truth (< 2mm) generated from a LiDAR. The authors scanned 100 cluttered indoor and 80 outdoor scenes featuring challenging environments and conditions. CATS contains approximately 1400 images of pedestrians, vehicles, electronics, and other thermally interesting objects in different environmental conditions, including nighttime, daytime, and foggy scenes.

Source: [CATS: A Color and Thermal Stereo Benchmark](/paper/cats-a-color-and-thermal-stereo-benchmark)

Introducing paper: CATS: A Color and Thermal Stereo Benchmark
caWaC

The corpus represents the largest existing corpus of Catalan containing 687 million words, which is a significant increase given that until now the biggest corpus of Catalan, CuCWeb, counts 166 million words. 

Source: [caWaC -- A web corpus of Catalan and its application to language modeling and machine translation](/paper/cawac-a-web-corpus-of-catalan-and-its)

Introducing paper: caWaC -- A web corpus of Catalan and its application to language modeling and machine translation
CC100

This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages. This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository.

Source: [Unsupervised Cross-lingual Representation Learning at Scale](/paper/unsupervised-cross-lingual-representation-1)

Introducing paper: Unsupervised Cross-lingual Representation Learning at Scale
CC-19

**CC-19** is a small new dataset related to the latest family of coronavirus i.e. COVID-19. The proposed dataset “CC-19” contains 34,006 CT scan slices (images) belonging to 98 subjects out of which 28,395 CT scan slices belong to positive COVID patients.

Source: [https://github.com/abdkhanstd/COVID-19](https://github.com/abdkhanstd/COVID-19)
Image Source: [https://github.com/abdkhanstd/COVID-19](https://github.com/abdkhanstd/COVID-19)

Introducing paper: Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging
CCD
Full name: Car Crash Dataset

**Car Crash Dataset** (**CCD**) is collected for traffic accident analysis. It contains real traffic accident videos captured by dashcam mounted on driving vehicles, which is critical to developing safety-guaranteed self-driving systems. CCD is distinguished from existing datasets for diversified accident annotations, including environmental attributes (day/night, snowy/rainy/good weather conditions), whether ego-vehicles involved, accident participants, and accident reason descriptions.

Source: [https://github.com/Cogito2012/CarCrashDataset](https://github.com/Cogito2012/CarCrashDataset)
Image Source: [https://github.com/Cogito2012/CarCrashDataset](https://github.com/Cogito2012/CarCrashDataset)

Introducing paper: Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning
CCPD
Full name: Chinese City Parking Dataset

The **Chinese City Parking Dataset** (**CCPD**) is a dataset for license plate detection and recognition. It contains over 250k unique car images, with license plate location annotations.

Source: [Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline](https://openaccess.thecvf.com/content_ECCV_2018/papers/Zhenbo_Xu_Towards_End-to-End_License_ECCV_2018_paper.pdf)
Image Source: [Xu et al](https://openaccess.thecvf.com/content_ECCV_2018/papers/Zhenbo_Xu_Towards_End-to-End_License_ECCV_2018_paper.pdf)

Introducing paper: Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline
Polish CDSCorpus

Consists of 10K sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish.

Source: [Polish evaluation dataset for compositional distributional semantics models](/paper/polish-evaluation-dataset-for-compositional)

Introducing paper: Polish evaluation dataset for compositional distributional semantics models
CECW
Full name: Colorful Extended Cleanup World

The **CECW** dataset is a color-extended version of the Cleanup World (CW) borrowed from the mobile-manipulation robot domain. CW refers to a world equipped with a movable object as well as four rooms in four colors, including "blue," "green," "red," and "yellow," which is designed as a simulation environment where the agent can act based on the instructions received. CW obeys a particular Geometric Linear Temporal Logic (GLTL) to parse commands by grammatical syntax, resulting in a total of 3,382 commands reflecting 39 GLTL expressions.

Source: [https://github.com/MrShininnnnn/CECW](https://github.com/MrShininnnnn/CECW)
Image Source: [https://github.com/MrShininnnnn/CECW](https://github.com/MrShininnnnn/CECW)

Introducing paper: Revisit Systematic Generalization via Meaningful Learning
CED
Full name: Color Event Camera Dataset

Contains 50 minutes of footage with both color frames and events. CED features a wide variety of indoor and outdoor scenes.

Source: [CED: Color Event Camera Dataset](/paper/ced-color-event-camera-dataset)

Introducing paper: CED: Color Event Camera Dataset
CelebA-Spoof

CelebA-Spoof is a large-scale face anti-spoofing dataset with the following properties: 

1. Quantity: CelebA-Spoof comprises of 625,537 pictures of 10,177 subjects, significantly larger than the existing datasets. 
2. Diversity: The spoof images are captured from 8 scenes (2 environments * 4 illumination conditions) with more than 10 sensors. 
3. Annotation Richness: CelebA-Spoof contains 10 spoof type annotations, as well as the 40 attribute annotations inherited from the original CelebA dataset.

Source: [CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations](/paper/celeba-spoof-large-scale-face-anti-spoofing)

Introducing paper: CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations
Cervix93 Cytology Dataset

The dataset has 93 image stacks and their corresponding Extended Depth of Field (EDF) image acquired from cases with grades Nagative, LSIL or HSIL (The Bethesda System):
- Negative: 16
- LSIL: 46
- HSIL: 31
The ground truth includes the grade labels for each frame and manually marked points inside cervical cells in each frame. There are in total 2705 manually marked points inside all frames:
- Negative: 238
- LSIL: 1536
- HSIL: 931

Source: [https://github.com/parham-ap/cytology_dataset](https://github.com/parham-ap/cytology_dataset)

Introducing paper: A New Cervical Cytology Dataset for Nucleus Detection and Image Classification (Cervix93) and Methods for Cervical Nucleus Detection
CFQ
Full name: Compositional Freebase Questions

A large and realistic natural language question answering dataset.

Source: [Measuring Compositional Generalization: A Comprehensive Method on Realistic Data](/paper/measuring-compositional-generalization-a-1)

Introducing paper: Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
CDNET
Full name: Change Detection

A video database for testing change detection algorithms. 

Source: [CDNET](http://www.changedetection.net/)

Introducing paper: Targeted change detection in remote sensing images
CHECKED

Chinese dataset on COVID-19 misinformation. CHECKED provides ground-truth on credibility, carefully obtained by ensuring the specific sources are used. CHECKED includes microblogs related to COVID-19, identified by using a specific list of keywords, covering a total 2120 microblogs published from December 2019 to August 2020. The dataset contains a rich set of multimedia information for each microblog including ground-truth label, textual, visual, response, and social network information.

Source: [CHECKED: Chinese COVID-19 Fake News Dataset](/paper/checked-chinese-covid-19-fake-news-dataset)

Introducing paper: CHECKED: Chinese COVID-19 Fake News Dataset
Chinese AI and Law (CAIL) 2018

Large-scale Chinese legal dataset for judgment prediction. \dataset contains more than 2.6  million criminal cases published by the Supreme People's Court of China, which are several times larger than other datasets in existing works on judgment prediction.

Source: [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](/paper/cail2018-a-large-scale-legal-dataset-for)

Introducing paper: CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction
ChineseFoodNet

ChineseFoodNet aims to automatically recognizing pictured Chinese dishes. Most of the existing food image datasets collected food images either from recipe pictures or selfie. In the dataset, images of each food category of the dataset consists of not only web recipe and menu pictures but photos taken from real dishes, recipe and menu as well. ChineseFoodNet contains over 180,000 food photos of 208 categories, with each category covering a large variations in presentations of same Chinese food. 

Source: [ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition](/paper/chinesefoodnet-a-large-scale-image-dataset)
Image Source: [https://sites.google.com/view/chinesefoodnet](https://sites.google.com/view/chinesefoodnet)

Introducing paper: ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition
Chinese Literature NER RE

Chinese Literature NER RE is a Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text. It is constructed from hundreds of Chinese literature articles.

Source: [https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset](https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset)
Image Source: [https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset](https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset)

Introducing paper: A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text
Chinese Traditional Painting dataset

The **Chinese Traditional Painting dataset** for style transfer contains 1000 content images and 100 style images.
The content images are mostly the photorealistic scenes of mountain, lake, river, bridge, and buildings in regions south of the Yangtze River. It includes not only the scenes of China, but also beautiful pictures of Rhine, Alps, Yellow Stone, Grand Canyon, etc. The content images include diverse types of Chinese traditional paintings.

Source: [https://github.com/lbsswu/Chinese_style_transfer](https://github.com/lbsswu/Chinese_style_transfer)
Image Source: [https://github.com/lbsswu/Chinese_style_transfer](https://github.com/lbsswu/Chinese_style_transfer)

Introducing paper: Neural Abstract Style Transfer for Chinese Traditional Painting
CIC
Full name: Catalonia Independence Corpus

The dataset is annotated with stance towards one topic, namely, the independence of Catalonia.

Source: [Multilingual Stance Detection: The Catalonia Independence Corpus](/paper/multilingual-stance-detection-the-catalonia)

Introducing paper: Multilingual Stance Detection: The Catalonia Independence Corpus
CITE

CITE is a crowd-sourced resource for multimodal discourse: this resource characterises inferences in image-text contexts in the domain of cooking recipes in the form of coherence relations.

Source: [CITE: A Corpus of Image-Text Discourse Relations](/paper/cite-a-corpus-of-image-text-discourse)

Introducing paper: CITE: A Corpus of Image-Text Discourse Relations
CITIUS Video Database

This eye tracking video database can be used to validate visual attention models. This dataset includes 72 videos downloaded from Internet and some synthetic videos generated in the lab. The videos can be classified in four categories, natural and synthetic, with fixed or movement camera. It includes 27 synthetic videos with dynamic pop-out effects. The videos have been selected in order to minimize the influence of the top-down effects.

Source: [CITIUS Video Database](https://wiki.citius.usc.es/aws-d:citius_video_database)
CITR Dataset

**CITR Dataset** consists of experimentally designed fundamental VCI scenarios (front, back, and lateral VCIs) and provides unique ID for each pedestrian, which is suitable for exploring a specific aspect of VCI. DUT dataset gives two ordinary and natural VCI scenarios in crowded university campus, which can be used for more general purpose VCI exploration.

Source: [Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus](/paper/top-view-trajectories-a-pedestrian-dataset-of)
Image Source: [Yang et al](https://arxiv.org/pdf/1902.00487v2.pdf)

Introducing paper: Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus
Cityscapes Panoptic Parts

The Cityscapes Panoptic Parts dataset introduces part-aware panoptic segmentation annotations for the Cityscapes dataset. It extends the original panoptic annotations for the Cityscapes dataset with part-level annotations for selected scene-level classes. 

Source: [https://arxiv.org/pdf/2004.07944.pdf](https://arxiv.org/pdf/2004.07944.pdf)
Image Source: [https://github.com/pmeletis/panoptic_parts](https://github.com/pmeletis/panoptic_parts)

Introducing paper: Part-aware Panoptic Segmentation
CLAD
Full name: Complex and Long Activities Dataset

CLAD (Compled and Long Activities Dataset) is an activity dataset which exhibits real-life and diverse scenarios of complex, temporally-extended human activities and actions. The dataset consists of a set of videos of actors performing everyday activities in a natural and unscripted manner. The dataset was recorded using a static Kinect 2 sensor which is commonly used on many robotic platforms. The dataset comprises of RGB-D images, point cloud data, automatically generated skeleton tracks in addition to crowdsourced annotations. 

Source: [Complex and Long Activities Dataset](http://archive.researchdata.leeds.ac.uk/254/)

Introducing paper: CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations