Omkar2118/datasets huggingface dataset:
Blog Authorship Corpus

The Blog Authorship Corpus consists of the collected posts of 19,320 bloggers gathered from blogger.com in August 2004. The corpus incorporates a total of 681,288 posts and over 140 million words - or approximately 35 posts and 7250 words per person.  

Source: [Blog Authorship Corpus](https://u.cs.biu.ac.il/~koppel/BlogCorpus.htm)
BLVD

BLVD is a large scale 5D semantics dataset collected by the Visual Cognitive Computing and Intelligent Vehicles Lab.
This dataset contains 654 high-resolution video clips owing 120k frames extracted from Changshu, Jiangsu Province, China, where the Intelligent Vehicle Proving Center of China (IVPCC) is located. The frame rate is 10fps/sec for RGB data and 3D point cloud. The dataset contains fully annotated frames which yield 249,129 3D annotations, 4,902 independent individuals for tracking with the length of overall 214,922 points, 6,004 valid fragments for 5D interactive event recognition, and 4,900 individuals for 5D intention prediction. These tasks are contained in four kinds of scenarios depending on the object density (low and high) and light conditions (daytime and nighttime).

Source: [BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving](/paper/blvd-building-a-large-scale-5d-semantics)

Introducing paper: BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving
Book Cover Dataset

A new challenging dataset that can be used for many pattern recognition tasks.

Source: [Judging a Book By its Cover](/paper/judging-a-book-by-its-cover)

Introducing paper: Judging a Book By its Cover
BotNet

The **BotNet** dataset is a set of topological botnet detection datasets forgraph neural networks.

Source: [https://github.com/harvardnlp/botnet-detection](https://github.com/harvardnlp/botnet-detection)
Image Source: [https://github.com/harvardnlp/botnet-detection](https://github.com/harvardnlp/botnet-detection)

Introducing paper: Automating Botnet Detection with Graph Neural Networks
BreizhCrops

**BreizhCrops** is a satellite image time series dataset for crop type classification. It consists on aggregated label data and Sentinel-2 top-of-atmosphere as well as bottom-of-atmosphere time series in the region of Brittany (Breizh in local language), north-east France.

Source: [https://github.com/TUM-LMF/BreizhCrops](https://github.com/TUM-LMF/BreizhCrops)

Introducing paper: BreizhCrops: A Time Series Dataset for Crop Type Mapping
Brno-Urban-Dataset

This self-driving dataset collected in Brno, Czech Republic contains data from four WUXGA cameras, two 3D LiDARs, inertial measurement unit, infrared camera and especially differential RTK GNSS receiver with centimetre accuracy.

Source: [https://arxiv.org/abs/1909.06897](https://arxiv.org/abs/1909.06897)
Image Source: [https://github.com/RoboticsBUT/Brno-Urban-Dataset](https://github.com/RoboticsBUT/Brno-Urban-Dataset)

Introducing paper: Brno Urban Dataset -- The New Data for Self-Driving Agents and Mapping Tasks
BRWAC

Composed by 2.7 billion tokens, and has been annotated with tagging and parsing information. 

Source: [The brWaC Corpus: A New Open Resource for Brazilian Portuguese](/paper/the-brwac-corpus-a-new-open-resource-for)

Introducing paper: The brWaC Corpus: A New Open Resource for Brazilian Portuguese
BSTLD
Full name: Bosch Small Traffic Lights Dataset

This dataset contains 13427 camera images at a resolution of 1280x720 pixels and contains about 24000 annotated traffic lights. The annotations include bounding boxes of traffic lights as well as the current state (active light) of each traffic light.
The camera images are provided as raw 12bit HDR images taken with a red-clear-clear-blue filter and as reconstructed 8-bit RGB color images. The RGB images are provided for debugging and can also be used for training. However, the RGB conversion process has some drawbacks. Some of the converted images may contain artifacts and the color distribution may seem unusual.

Source: [BSTLD](https://hci.iwr.uni-heidelberg.de/node/6132)
BVI-DVC

Contains 800 sequences at various spatial resolutions from 270p to 2160p and has been evaluated on ten existing network architectures for four different coding tools.

Source: [BVI-DVC: A Training Database for Deep Video Compression](/paper/bvi-dvc-a-training-database-for-deep-video)

Introducing paper: BVI-DVC: A Training Database for Deep Video Compression
C3

C3 is a free-form multiple-Choice Chinese machine reading Comprehension dataset.

Introducing paper: Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension
CADC
Full name: Canadian Adverse Driving Conditions

Collected with the Autonomoose autonomous vehicle platform, based on a modified Lincoln MKZ.

Source: [Canadian Adverse Driving Conditions Dataset](/paper/canadian-adverse-driving-conditions-dataset)

Introducing paper: Canadian Adverse Driving Conditions Dataset
CADP

A novel dataset for traffic accidents analysis. 

Source: [CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis](/paper/cadp-a-novel-dataset-for-cctv-traffic-camera)

Introducing paper: CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis
CAIL2019-SCM

Chinese AI and Law 2019 Similar Case Matching dataset. CAIL2019-SCM contains 8,964 triplets of cases published by the Supreme People's Court of China. CAIL2019-SCM focuses on detecting similar cases, and the participants are required to check which two cases are more similar in the triplets.

Source: [CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain](/paper/cail2019-scm-a-dataset-of-similar-case)

Introducing paper: CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain
CALFW
Full name: Cross-Age LFW

A renovation of Labeled Faces in the Wild (LFW), the de facto standard testbed for unconstraint face verification. 

Source: [CALFW](http://whdeng.cn/CALFW/index.html)
Caltech Pedestrian Dataset

The Caltech Pedestrian Dataset consists of approximately 10 hours of 640x480 30Hz video taken from a vehicle driving through regular traffic in an urban environment. About 250,000 frames (in 137 approximately minute long segments) with a total of 350,000 bounding boxes and 2300 unique pedestrians were annotated. The annotation includes temporal correspondence between bounding boxes and detailed occlusion labels.

Source: [Caltech Pedestrian Dataset](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/)
capes

Approximately 240,000 documents were collected and aligned using the Hunalign tool.

Source: [A Parallel Corpus of Theses and Dissertations Abstracts](/paper/a-parallel-corpus-of-theses-and-dissertations)

Introducing paper: A Parallel Corpus of Theses and Dissertations Abstracts
CapGaze

Consists of eye movements and verbal descriptions recorded synchronously over images.

Source: [Human Attention in Image Captioning: Dataset and Analysis](/paper/a-synchronized-multi-modal-attention-caption)

Introducing paper: Human Attention in Image Captioning: Dataset and Analysis
CARD-660

An expert-annotated word similarity dataset which provides a highly reliable, yet challenging, benchmark for rare word representation techniques. 

Source: [Card-660: Cambridge Rare Word Dataset - a Reliable Benchmark for Infrequent Word Representation Models](/paper/card-660-cambridge-rare-word-dataset-a)

Introducing paper: Card-660: Cambridge Rare Word Dataset - a Reliable Benchmark for Infrequent Word Representation Models
CARRADA

CARRADA is a dataset of synchronized camera and radar recordings with range-angle-Doppler annotations. 

Source: [CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations](/paper/carrada-dataset-camera-and-automotive-radar)
Image Source: [https://github.com/valeoai/carrada_dataset](https://github.com/valeoai/carrada_dataset)

Introducing paper: CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations
CASIA-SURF

Dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of  subjects with  videos and each sample has  modalities (i.e., RGB, Depth and IR). 

Source: [CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing](/paper/casia-surf-a-large-scale-multi-modal)

Introducing paper: CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing