Data Augmentation

The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection. The publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld. ILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”. The ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided. * Total number of non-empty WordNet synsets: 21841 * Total number of images: 14197122 * Number of images with bounding box annotations: 1,034,908 * Number of synsets with SIFT features: 1000 * Number of images with SIFT features: 1.2 million Source: ImageNet Large Scale Visual Recognition Challenge Image Source: https://cs.stanford.edu/people/karpathy/cnnembed/

The ESC-50 dataset is a labeled collection of 2000 environmental audio recordings suitable for benchmarking methods of environmental sound classification. It comprises 2000 5s-clips of 50 different classes across natural, human and domestic sounds, again, drawn from Freesound.org. Source: The NIGENS General Sound Events Database Image Source: https://github.com/karolpiczak/ESC-50

ImageNet-Sketch data set consists of 50,889 images, approximately 50 images for each of the 1000 ImageNet classes. The data set is constructed with Google Image queries "sketch of __", where __ is the standard class name. Only within the "black and white" color scheme is searched. 100 images are initially queried for every class, and the pulled images are cleaned by deleting the irrelevant images and images that are for similar but different classes. For some classes, there are less than 50 images after manually cleaning, and then the data set is augmented by flipping and rotating the images. Source: ImageNet-Sketch Image Source: https://github.com/HaohanWang/ImageNet-Sketch

MuST-C currently represents the largest publicly available multilingual corpus (one-to-many) for speech translation. It covers eight language directions, from English to German, Spanish, French, Italian, Dutch, Portuguese, Romanian and Russian. The corpus consists of audio, transcriptions and translations of English TED talks, and it comes with a predefined training, validation and test split. Source: One-to-Many Multilingual End-to-End Speech Translation Image Source: https://mt.fbk.eu/must-c

Clotho is an audio captioning dataset, consisting of 4981 audio samples, and each audio sample has five captions (a total of 24 905 captions). Audio samples are of 15 to 30 s duration and captions are eight to 20 words long. Source: https://zenodo.org/record/3490684 Image Source: https://arxiv.org/abs/1910.09387

The IAM database contains 13,353 images of handwritten lines of text created by 657 writers. The texts those writers transcribed are from the Lancaster-Oslo/Bergen Corpus of British English. It includes contributions from 657 writers making a total of 1,539 handwritten pages comprising of 115,320 words and is categorized as part of modern collection. The database is labeled at the sentence, line, and word levels. Source: Measuring Human Perception to Improve Handwritten Document Transcription Image Source: https://fki.tic.heia-fr.ch/databases/iam-handwriting-database

MathQA significantly enhances the AQuA dataset with fully-specified operational programs. Source: MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms

Paraphrase Adversaries from Word Scrambling (PAWS) is a dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature the importance of modeling structure, context, and word order information for the problem of paraphrase identification. The dataset has two subsets, one based on Wikipedia and the other one based on the Quora Question Pairs (QQP) dataset. Source: PAWS

Urban Sound 8K is an audio dataset that contains 8732 labeled sound excerpts (<=4s) of urban sounds from 10 classes: air_conditioner, car_horn, children_playing, dog_bark, drilling, enginge_idling, gun_shot, jackhammer, siren, and street_music. The classes are drawn from the urban sound taxonomy. All excerpts are taken from field recordings uploaded to www.freesound.org. Source: https://zenodo.org/record/401395 Image Source: https://urbansounddataset.weebly.com/urbansound8k.html

GAP is a gender-balanced dataset containing 8,908 coreference-labeled pairs of (ambiguous pronoun, antecedent name), sampled from Wikipedia and released by Google AI Language for the evaluation of coreference resolution in practical applications. Source: GAP Coreference Dataset Image Source: https://arxiv.org/pdf/1810.05201.pdf

The PROMISE12 dataset was made available for the MICCAI 2012 prostate segmentation challenge. Magnetic Resonance (MR) images (T2-weighted) of 50 patients with various diseases were acquired at different locations with several MRI vendors and scanning protocols. Source: Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions Image Source: https://promise12.grand-challenge.org/

SParC is a large-scale dataset for complex, cross-domain, and context-dependent (multi-turn) semantic parsing and text-to-SQL task (interactive natural language interfaces for relational databases). Source: https://github.com/taoyds/sparc Image Source: https://arxiv.org/pdf/1906.02285.pdf

Europarl-ST is a multilingual Spoken Language Translation corpus containing paired audio-text samples for SLT from and into 9 European languages, for a total of 72 different translation directions. This corpus has been compiled using the debates held in the European Parliament in the period between 2008 and 2012. Source: Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debates

The xBD dataset contains over 45,000KM2 of polygon labeled pre and post disaster imagery. The dataset provides the post-disaster imagery with transposed polygons from pre over the buildings, with damage classification labels. Source: xBD Image Source: Gupta et al

The Synthesized Lakh (Slakh) Dataset is a dataset for audio source separation that is synthesized from the Lakh MIDI Dataset v0.1 using professional-grade sample-based virtual instruments. This first release of Slakh, called Slakh2100, contains 2100 automatically mixed tracks and accompanying MIDI files synthesized using a professional-grade sampling engine. The tracks in Slakh2100 are split into training (1500 tracks), validation (375 tracks), and test (225 tracks) subsets, totaling 145 hours of mixtures. Source: http://www.slakh.com/ Image Source: http://www.slakh.com/ Audio Source: http://www.slakh.com/

Contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of COVID-19. Source: COVID-19 Image Data Collection

MeQSum is a dataset for medical question summarization. It contains 1,000 summarized consumer health questions. Source: https://www.aclweb.org/anthology/P19-1215.pdf Image Source: https://www.aclweb.org/anthology/P19-1215.pdf

MedQuAD includes 47,457 medical question-answer pairs created from 12 NIH websites (e.g. cancer.gov, niddk.nih.gov, GARD, MedlinePlus Health Topics). The collection covers 37 question types (e.g. Treatment, Diagnosis, Side Effects) associated with diseases, drugs and other medical entities such as tests. Source: A Question-Entailment Approach to Question Answering

The HELP dataset is an automatically created natural language inference (NLI) dataset that embodies the combination of lexical and logical inferences focusing on monotonicity (i.e., phrase replacement-based reasoning). The HELP (Ver.1.0) has 36K inference pairs consisting of upward monotone, downward monotone, non-monotone, conjunction, and disjunction. Source: HELP

The MRNet dataset consists of 1,370 knee MRI exams performed at Stanford University Medical Center. The dataset contains 1,104 (80.6%) abnormal exams, with 319 (23.3%) ACL tears and 508 (37.1%) meniscal tears; labels were obtained through manual extraction from clinical reports. Source: MRNet

COunter NArratives through Nichesourcing (CONAN) is a dataset that consists of 4,078 pairs over the 3 languages. Additionally, 3 types of metadata are provided: expert demographics, hate speech sub-topic and counter-narrative type. The dataset is augmented through translation (from Italian/French to English) and paraphrasing, which brought the total number of pairs to 14.988. Source: CONAN Image Source: https://www.aclweb.org/anthology/P19-1271

Evidence Inference is a corpus for this task comprising 10,000+ prompts coupled with full-text articles describing RCTs. Source: Inferring Which Medical Treatments Work from Reports of Clinical Trials

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 Image Source: Xu et al

KdConv is a Chinese multi-domain Knowledge-driven Conversation dataset, grounding the topics in multi-turn conversations to knowledge graphs. KdConv contains 4.5K conversations from three domains (film, music, and travel), and 86K utterances with an average turn number of 19.0. These conversations contain in-depth discussions on related topics and natural transition between multiple topics, while the corpus can also used for exploration of transfer learning and domain adaptation. Source: KdConv

ETHOS is a hate speech detection dataset. It is built from YouTube and Reddit comments validated through a crowdsourcing platform. It has two subsets, one for binary classification and the other for multi-label classification. The former contains 998 comments, while the latter contains fine-grained hate-speech annotations for 433 comments. Source: https://arxiv.org/abs/2006.08328

The George Washington dataset contains 20 pages of letters written by George Washington and his associates in 1755 and thereby categorized into historical collection. The images are annotated at word level and contain approximately 5,000 words. Source: HWNet v2: An Efficient Word Image Representation for Handwritten Documents. Image Source: https://www.loc.gov/resource/mgw1a.002/?sp=2

InfoTabS comprises of human-written textual hypotheses based on premises that are tables extracted from Wikipedia info-boxes. Source: INFOTABS: Inference on Tables as Semi-structured Data

MED is a new evaluation dataset that covers a wide range of monotonicity reasoning that was created by crowdsourcing and collected from linguistics publications. The dataset was constructed by collecting naturally-occurring examples by crowdsourcing and well-designed ones from linguistics publications. It consists of 5,382 examples. Source: https://github.com/verypluming/MED Image Source: https://www.aclweb.org/anthology/W19-4804v2.pdf

ORCAS is a click-based dataset. It covers 1.4 million of the TREC DL documents, providing 18 million connections to 10 million distinct queries.

RPC is a large-scale retail product checkout dataset and collects 200 retail SKUs. The collected SKUs can be divided into 17 meta categories, i.e., puffed food, dried fruit, dried food, instant drink, instant noodles, dessert, drink, alcohol, milk, canned food, chocolate, gum, candy, seasoner, personal hygiene, tissue, stationery. Source: RPC: A Large-Scale Retail Product Checkout Dataset Image Source: Wei et al

VehicleX is a large-scale synthetic dataset. Created in Unity, it contains 1,362 vehicles of various 3D models with fully editable attributes. Source: https://github.com/yorkeyao/VehicleX Image Source: https://arxiv.org/pdf/1912.08855.pdf

BRATS 2016 is a brain tumor segmentation dataset. It shares the same training set as BRATS 2015, which consists of 220 HHG and 54 LGG. Its testing dataset consists of 191 cases with unknown grades. Image Source: https://sites.google.com/site/braintumorsegmentation/home/brats_2016

Logo-2K+:A Large-Scale Logo Dataset for Scalable Logo Classification The Logo-2K+ dataset contains a diverse range of logo classes from real-world logo images. It contains 167,140 images with 10 root categories and 2,341 leaf categories. The 10 different root categories are: Food, Clothes, Institution, Accessories, Transportation, Electronic, Necessities, Cosmetic, Leisure and Medical. Source: https://github.com/msn199959/Logo-2k-plus-Dataset Image Source: https://github.com/msn199959/Logo-2k-plus-Dataset

The dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding abstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player collaboration and spoken interaction. Source: Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset

EgoHOS is a labeled dataset consisting of 11243 egocentric images with per-pixel segmentation labels of hands and objects being interacted with during a diverse array of daily activities. The data are collected form multiple sources: 7,458 frames from Ego4D, 2,212 frames from EPIC-KITCHEN, 806 frames from THU-READ, and 350 frames of our own collected egocentric videos with people playing Escape Room. This dataset is designed for tasks including hand state classification, video activity recognition, 3D mesh reconstruction of hand-object interactions, and video inpainting of hand-object foregrounds in egocentric videos. Source: Fine-grained Egocentric Hand-object Segmentation: Dataset, Model, and Applications Image Source: https://arxiv.org/pdf/2208.03826v1.pdf