Omkar2118/datasets huggingface dataset:
CSS10

A collection of single speaker speech datasets for ten languages. It is composed of short audio clips from LibriVox audiobooks and their aligned texts.

Source: [CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages](/paper/css10-a-collection-of-single-speaker-speech)

Introducing paper: CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages
CTC
Full name: COCO-Text Captioned

A dataset that allows exploration of cross-modal retrieval where images contain scene-text instances. 

Source: [StacMR: Scene-Text Aware Cross-Modal Retrieval](/paper/stacmr-scene-text-aware-cross-modal-retrieval)

Introducing paper: StacMR: Scene-Text Aware Cross-Modal Retrieval
CTPelvic1K

Curates a large pelvic CT dataset pooled from multiple sources and different manufacturers, including 1, 184 CT volumes and over 320, 000 slices with different resolutions and a variety of the above-mentioned appearance variations.

Source: [Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models](/paper/deep-learning-to-segment-pelvic-bones-large)

Introducing paper: Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models
Cube++

**Cube++** is a novel dataset for the color constancy problem that continues on the Cube+ dataset. It includes 4890 images of different scenes under various conditions. For calculating the ground truth illumination, a calibration object with known surface colors was placed in every scene.

Source: [https://github.com/Visillect/CubePlusPlus](https://github.com/Visillect/CubePlusPlus)
Image Source: [https://github.com/Visillect/CubePlusPlus](https://github.com/Visillect/CubePlusPlus)

Introducing paper: The Cube++ Illumination Estimation Dataset
CubiCasa5K

**CubiCasa5K** is a large-scale floorplan image dataset containing 5000 samples annotated into over 80 floorplan object categories. The dataset annotations are performed in a dense and versatile manner by using polygons for separating the different objects.

Source: [https://github.com/CubiCasa/CubiCasa5k](https://github.com/CubiCasa/CubiCasa5k)

Introducing paper: CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis
CUHK-QA

CUHK-QA is a dataset for natural language-based person search using iterative questioning. 

The dataset consists of 400 images of 360 people, and  20 participants answered 5 specific questions about the appearance of each person. So, each person has labelled 20 images. Average length of combined description for each image is 39.15. All the images have been taken from the test set of CUHK-PEDES dataset.

Source: [Interactive Natural Language-based Person Search](/paper/interactive-natural-language-based-person)

Introducing paper: Interactive Natural Language-based Person Search
CUHK-Shadow

Collects shadow images for multiple scenarios and compiled a new dataset of 10,500 shadow images, each with labeled ground-truth mask, for supporting shadow detection in the complex world. The dataset covers a rich variety of scene categories, with diverse shadow sizes, locations, contrasts, and types. 

Source: [Revisiting Shadow Detection: A New Benchmark Dataset for Complex World](/paper/revisiting-shadow-detection-a-new-benchmark)

Introducing paper: Revisiting Shadow Detection: A New Benchmark Dataset for Complex World
Cumulo

A benchmark dataset for training and evaluating global cloud classification models. It consists of one year of 1km resolution MODIS hyperspectral imagery merged with pixel-width 'tracks' of CloudSat cloud labels. 

Source: [Cumulo: A Dataset for Learning Cloud Classes](/paper/cumulo-a-dataset-for-learning-cloud-classes)

Introducing paper: Cumulo: A Dataset for Learning Cloud Classes
Curated AFD

The **Curated AFD** dataset is a curated version of the Asian Face Dataset (AFD) for face recognition research. The original AFD dataset has been curated to remove wrong identity labels, duplicate images and duplicate subjects.

Source: [https://arxiv.org/abs/2004.03074](https://arxiv.org/abs/2004.03074)

Introducing paper: A Method for Curation of Web-Scraped Face Image Datasets
Curation Corpus

The Curation Corpus is a collection of 40,000 professionally-written summaries of news articles, with links to the articles themselves. 

Source: [Curation Corpus](https://github.com/CurationCorp/curation-corpus)
CURE-TSD
Full name: CURE Traffic Sign Detection

Based on simulated challenging conditions that correspond to adversaries that can occur in real-world environments and systems. 

Source: [Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics](/paper/traffic-sign-detection-under-challenging)

Introducing paper: Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics
Curiosity

The **Curiosity** dataset consists of 14K dialogs (with 181K utterances) with fine-grained knowledge groundings, dialog act annotations, and other auxiliary annotation. In this dataset users and virtual assistants converse about geographic topics like geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog acts, grounding to Wikipedia, and user reactions to messages.

Source: [https://github.com/facebookresearch/curiosity](https://github.com/facebookresearch/curiosity)
Image Source: [https://www.pedro.ai/curiosity](https://www.pedro.ai/curiosity)

Introducing paper: Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity
Czech restaurant information

Czech restaurant information is a dataset for NLG in task-oriented spoken dialogue systems with Czech as the target language. It originated as a translation of the English San Francisco Restaurants dataset by Wen et al. (2015).

Source: [https://github.com/UFAL-DSG/cs_restaurant_dataset](https://github.com/UFAL-DSG/cs_restaurant_dataset)

Introducing paper: Neural Generation for Czech: Data and Baselines
CzEng 2.0 Parallel Corpus

Czech-English parallel corpus CzEng 2.0 consisting of over 2 billion words (2 "gigawords") in each language. The corpus contains document-level information and is filtered with several techniques to lower the amount of noise.

Source: [Announcing CzEng 2.0 Parallel Corpus with over 2 Gigawords](/paper/announcing-czeng-2-0-parallel-corpus-with)

Introducing paper: Announcing CzEng 2.0 Parallel Corpus with over 2 Gigawords
D2City

A large-scale comprehensive collection of dashcam videos collected by vehicles on DiDi's platform. D2-City contains more than 10000 video clips which deeply reflect the diversity and complexity of real-world traffic scenarios in China.

Source: [D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios](/paper/d2-city-a-large-scale-dashcam-video-dataset)

Introducing paper: D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios
DAD
Full name: Driver Anomaly Detection

Contains normal driving videos together with a set of anomalous actions in its training set. In the test set of the DAD dataset, there are unseen anomalous actions that still need to be winnowed out from normal driving. 

Source: [Driver Anomaly Detection: A Dataset and Contrastive Learning Approach](/paper/driver-anomaly-detection-a-dataset-and)

Introducing paper: Driver Anomaly Detection: A Dataset and Contrastive Learning Approach
DAIS

A large benchmark dataset containing 50K human judgments for 5K distinct sentence pairs in the English dative alternation. This dataset includes 200 unique verbs and systematically varies the definiteness and length of arguments. 

Source: [Investigating representations of verb bias in neural language models](/paper/investigating-representations-of-verb-bias-in)

Introducing paper: Investigating representations of verb bias in neural language models
DAiSEE

DAiSEE is a multi-label video classification dataset comprising of 9,068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration "in the wild". The dataset has four levels of labels namely - very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. 

Source: [DAiSEE: Towards User Engagement Recognition in the Wild](/paper/daisee-towards-user-engagement-recognition-in)

Introducing paper: DAiSEE: Towards User Engagement Recognition in the Wild