Omkar2118/datasets huggingface dataset:
KenyanFood13

The Kenyan Food Type Dataset (**KenyanFood13**) is an image classification dataset for Kenyan food. The images are categorized into 13 different labels.

Source: [https://github.com/monajalal/Kenyan-Food](https://github.com/monajalal/Kenyan-Food)
Image Source: [https://github.com/monajalal/Kenyan-Food](https://github.com/monajalal/Kenyan-Food)

Introducing paper: Scraping Social Media Photos Posted in Kenya and Elsewhere to Detect and Analyze Food Types
KeypointNet

**KeypointNet** is a large-scale and diverse 3D keypoint dataset that contains 83,231 keypoints and 8,329 3D models from 16 object categories, by leveraging numerous human annotations, based on ShapeNet models.

Source: [https://github.com/qq456cvb/KeypointNet](https://github.com/qq456cvb/KeypointNet)
Image Source: [https://github.com/qq456cvb/KeypointNet](https://github.com/qq456cvb/KeypointNet)

Introducing paper: KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations
KinectFaceDB

The Dataset consists of the multimodal facial images of 52 people (14 females, 38 males) obtained by Kinect. The data is captured in two sessions happened at different time period (about half month). In each session, the dataset provides the facial images of each person in 9 states of different facial expressions, different lighting and occlusion conditions: neutral, smile, open mouth, left profile, right profile, occlusion eyes, occlusion mouth, occlusion paper and light on [Figure 1]. All the images are provided in three sources of information: the RGB color image, the depth map (provided in two forms of the bitmap depth image and the text file containing the original depth levels sensed by Kinect) as well as 3D. In addition, the dataset comes with the manual landmarks of 6 positions in the face: left eye, right eye, the tip of nose, left side of mouth, right side of mouth and the chin [Figure 2]. Other information of the person such as gender, year of birth, glasses (this person wears the glasses or not), capture time of each session are also available.

Source: [KinectFaceDB](http://rgb-d.eurecom.fr/)
KINNEWS and KIRNEWS

Two news datasets (KINNEWS and KIRNEWS) for multi-class classification of news articles in Kinyarwanda and Kirundi, two low-resource African languages. The two languages are mutually intelligible.

Source: [KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi](/paper/kinnews-and-kirnews-benchmarking-cross)

Introducing paper: KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi
KINS

Augments the KITTI with more instance pixel-level annotation for 8 categories.

Source: [Amodal Instance Segmentation with KINS Dataset](http://jiaya.me/papers/amodel_cvpr19.pdf)
Kinteract

Explicitly created for Human Computer Interaction (HCI).

Source: [Fast Gesture Recognition with Multiple Stream Discrete HMMs on 3D Skeletons](/paper/fast-gesture-recognition-with-multiple-stream)

Introducing paper: Fast Gesture Recognition with Multiple Stream Discrete HMMs on 3D Skeletons
Kitchen Scenes

Kitchen Scenes is a multi-view RGB-D dataset of nine kitchen scenes, each containing several objects in realistic cluttered environments including a subset of objects from the BigBird dataset. The viewpoints of the scenes are densely sampled and objects in the scenes are annotated with bounding boxes and in the 3D point cloud. 

Source: [Multiview RGB-D Dataset for Object Instance Detection](/paper/multiview-rgb-d-dataset-for-object-instance)

Introducing paper: Multiview RGB-D Dataset for Object Instance Detection
KIT Motion-Language

The KIT Motion-Language is a dataset linking human motion and natural language.

Source: [The KIT Motion-Language Dataset](/paper/the-kit-motion-language-dataset)

Introducing paper: The KIT Motion-Language Dataset
KnowIT VQA

KnowIT VQA is a video dataset with 24,282 human-generated question-answer pairs about The Big Bang Theory. The dataset combines visual, textual and temporal coherence reasoning together with knowledge-based questions, which need of the experience obtained from the viewing of the series to be answered.

Source: [KnowIT VQA: Answering Knowledge-Based Questions about Videos](/paper/knowit-vqa-answering-knowledge-based)

Introducing paper: KnowIT VQA: Answering Knowledge-Based Questions about Videos
Korean HateSpeech Dataset

Presents 9.4K manually labeled entertainment news comments for identifying Korean toxic speech, collected from a widely used online news platform in Korea.

Source: [BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection](/paper/beep-korean-corpus-of-online-news-comments)

Introducing paper: BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection
KorNLI

**KorNLI** is a Korean Natural Language Inference (NLI) dataset. The dataset is constructed by automatically translating the training sets of the SNLI, XNLI and MNLI datasets. To ensure translation quality, two professional translators with at least seven years of experience who specialize in academic papers/books as well as business contracts post-edited a half of the dataset each and cross-checked each other’s translation afterward.
It contains 942,854 training examples translated automatically and 7,500 evaluation (development and test) examples translated manually

Source: [https://github.com/kakaobrain/KorNLUDatasets](https://github.com/kakaobrain/KorNLUDatasets)

Introducing paper: KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding
KPTimes

KPTimes is a large-scale dataset of news texts paired with editor-curated keyphrases. 

Source: [KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents](/paper/kptimes-a-large-scale-dataset-for-keyphrase-1)

Introducing paper: KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents
KQA Pro

A large-scale dataset for Complex KBQA.

Source: [KQA Pro: A Large-Scale Dataset with Interpretable Programs and Accurate SPARQLs for Complex Question Answering over Knowledge Base](/paper/kqa-pro-a-large-diagnostic-dataset-for)

Introducing paper: KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base
Kuzushiji-49

Kuzushiji-49 is an MNIST-like dataset that has 49 classes (28x28 grayscale, 270,912 images) from 48 Hiragana characters and one Hiragana iteration mark.

Source: [Deep Learning for Classical Japanese Literature](/paper/deep-learning-for-classical-japanese)
Image Source: [Clanuwat et al](https://arxiv.org/pdf/1812.01718v1.pdf)

Introducing paper: Deep Learning for Classical Japanese Literature
LAD
Full name: Large-scale Attribute Dataset

LAD (Large-scale Attribute Dataset) has 78,017 images of 5 super-classes and 230 classes. The image number of LAD is larger than the sum of the four most popular attribute datasets (AwA, CUB, aP/aY and SUN). 359 attributes of visual, semantic and subjective properties are defined and annotated in instance-level.

Source: [A Large-scale Attribute Dataset for Zero-shot Learning](/paper/a-large-scale-attribute-dataset-for-zero-shot)

Introducing paper: A Large-scale Attribute Dataset for Zero-shot Learning
LAG
Full name: Large-scale Attention based Glaucoma

Includes 5,824 fundus images labeled with either positive glaucoma (2,392) or negative glaucoma (3,432).

Source: [Attention Based Glaucoma Detection: A Large-scale Database and CNN Model](/paper/attention-based-glaucoma-detection-a-large)

Introducing paper: Attention Based Glaucoma Detection: A Large-scale Database and CNN Model
LaMem

An annotated image memorability dataset to date (with 60,000 labeled images from a diverse array of sources).

Source: [Understanding and Predicting Image Memorability at a Large Scale](/paper/understanding-and-predicting-image)

Introducing paper: Understanding and Predicting Image Memorability at a Large Scale
LAOFIW Dataset
Full name: Labeled Ancestral Origin Faces in the Wild

An ancestral origin database of 14,000 images of individuals from East Asia, the Indian subcontinent, sub-Saharan Africa, and Western Europe.

Source: [Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings](/paper/turning-a-blind-eye-explicit-removal-of)

Introducing paper: Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings
LaPa

A large-scale Landmark guided face Parsing dataset (LaPa) for face parsing. It consists of more than 22,000 facial images with abundant variations in expression, pose and occlusion, and each image of LaPa is provided with a 11-category pixel-level label map and 106-point landmarks.

Source: [LaPa](https://github.com/JDAI-CV/lapa-dataset)
LasVR

A large-scale video database for rain removal (LasVR), which consists of 316 rain videos.

Source: [Removing Rain in Videos: A Large-scale Database and A Two-stream ConvLSTM Approach](/paper/removing-rain-in-videos-a-large-scale)

Introducing paper: Removing Rain in Videos: A Large-scale Database and A Two-stream ConvLSTM Approach
Lazaro Corpus

A corpus of 21,570 newspaper headlines written in European Spanish annotated with emergent anglicisms.

Source: [An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines](/paper/an-annotated-corpus-of-emerging-anglicisms-in)

Introducing paper: An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines
LC25000
Full name: Lung And Colon Histopathological Image Dataset

The **LC25000** dataset contains 25,000 color images with 5 classes of 5,000 images each. All images are 768 x 768 pixels in size and are in jpeg file format. The 5 classes are: colon adenocarcinomas, benign colonic tissues, lung adenocarcinomas, lung squamous cell carcinomas and bening lung tissues.

Source: [https://github.com/tampapath/lung_colon_image_set](https://github.com/tampapath/lung_colon_image_set)

Introducing paper: Lung and Colon Cancer Histopathological Image Dataset (LC25000)
LCCC
Full name: Large-scale Cleaned Chinese Conversation corpus

Contains a base version (6.8million dialogues) and a large version (12.0 million dialogues). 

Source: [A Large-Scale Chinese Short-Text Conversation Dataset](/paper/a-large-scale-chinese-short-text-conversation)

Introducing paper: A Large-Scale Chinese Short-Text Conversation Dataset
LC-QuAD
Full name: Largescale Complex Question Answering Dataset

LC-QuAD is a Large Question Answering dataset with 30,000 pairs of questions and its corresponding SPARQL query. The target knowledge base is Wikidata and DBpedia, specifically the 2018 version.
LEAF Benchmark

A suite of open-source federated datasets, a rigorous evaluation framework, and a set of reference implementations, all geared towards capturing the obstacles and intricacies of practical federated environments.

Source: [LEAF: A Benchmark for Federated Settings](/paper/leaf-a-benchmark-for-federated-settings)

Introducing paper: LEAF: A Benchmark for Federated Settings
Leaf counting dataset

Dataset containing  9372 RGB images of weeds with the number of leaves counted. The images are collected in fields across Denmark using Nokia and Samsung cell phone cameras; Samsung, Nikon, Canon and Sony consumer cameras; and a Point Grey industrial camera.

Source: [Leaf counting dataset](https://vision.eng.au.dk/leaf-counting-dataset/)
LectureBank

**LectureBank** Dataset is a manually collected dataset of lecture slides. It contains 1,352 online lecture files from 60 courses covering 5 different domains, including Natural Language Processing (nlp), Machine Learning (ml), Artificial Intelligence (ai), Deep Learning (dl) and Information Retrieval (ir). In addition, it also contains the corresponding annotations for each slide.

Source: [https://github.com/Yale-LILY/LectureBank](https://github.com/Yale-LILY/LectureBank)
Image Source: [https://github.com/Yale-LILY/LectureBank](https://github.com/Yale-LILY/LectureBank)

Introducing paper: What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning
Legal Documents Entity Recognition

Court decisions from 2017 and 2018 were selected for the dataset, published online by the Federal Ministry of Justice and Consumer Protection. The documents originate from seven federal courts: Federal Labour Court (BAG), Federal Fiscal Court (BFH), Federal Court of Justice (BGH), Federal Patent Court (BPatG), Federal Social Court (BSG), Federal Constitutional Court (BVerfG) and Federal Administrative Court (BVerwG).

Source: [Legal Documents Entity Recognition](https://github.com/elenanereiss/Legal-Entity-Recognition)
LEMMA

The **LEMMA** dataset aims to explore the essence of complex human activities in a goal-directed, multi-agent, multi-task setting with ground-truth labels of compositional atomic-actions and their associated tasks. By quantifying the scenarios to up to two multi-step tasks with two agents, the authors strive to address human multi-task and multi-agent interactions in four scenarios: single-agent single-task (1 x 1), single-agent multi-task (1 x 2), multi-agent single-task (2 x 1), and multi-agent multi-task (2 x 2). Task instructions are only given to one agent in the 2 x 1 setting to resemble the robot-helping scenario, hoping that the learned perception models could be applied in robotic tasks (especially in HRI) in the near future.

Both the third-person views (TPVs) and the first-person views (FPVs) were recorded to account for different perspectives of the same activities. The authors densely annotate atomic-actions (in the form of compositional verb-noun pairs) and tasks of each atomic-action, as well as the spatial location of each participating agent (bounding boxes) to facilitate the learning of multi-agent multi-task task scheduling and assignment.

Source: [LEMMA](https://sites.google.com/view/lemma-activity/home)
Lenta Short Sentences

The **Lenta Short Sentences** dataset is a text dataset for language modelling for the Russian language. It consists of 236K sentences sampled from the Lenta News dataset.

Source: [https://arxiv.org/pdf/2005.02470.pdf](https://arxiv.org/pdf/2005.02470.pdf)

Introducing paper: Russian Natural Language Generation: Creation of a Language Modelling Dataset and Evaluation with Modern Neural Architectures
Libri-Adapt

Libri-Adapt aims to support unsupervised domain adaptation research on speech recognition models.

Source: [Libri-Adapt: A New Speech Dataset for Unsupervised Domain Adaptation](/paper/libri-adapt-a-new-speech-dataset-for)

Introducing paper: Libri-Adapt: A New Speech Dataset for Unsupervised Domain Adaptation
LibriCSS

Continuous speech separation (CSS) is an approach to handling overlapped speech in conversational audio signals. A real recorded dataset, called **LibriCSS**, is derived from LibriSpeech by concatenating the corpus utterances to simulate a conversation and capturing the audio replays with far-field microphones.

Source: [https://github.com/chenzhuo1011/libri_css](https://github.com/chenzhuo1011/libri_css)

Introducing paper: Continuous speech separation: dataset and analysis
LibriMix

LibriMix is an open-source alternative to wsj0-2mix. Based on LibriSpeech, LibriMix consists of two- or three-speaker mixtures combined with ambient noise samples from WHAM!. 

Source: [LibriMix: An Open-Source Dataset for Generalizable Speech Separation](/paper/librimix-an-open-source-dataset-for)

Introducing paper: LibriMix: An Open-Source Dataset for Generalizable Speech Separation
LiMiT
Full name: Literal Motion in Text Dataset

The limit dataset of ~24K sentences that describe literal motion (~14K sentences), and sentences not describing motion or other type of motion (e.g. fictive motion). Senteces were extracted from electronic books categorized as fiction or novels, and a portion from the NetActivity Captions Dataset.

Source: [LiMiT](https://github.com/ilmgut/limit_dataset)
LinCE
Full name: Linguistic Code-switching Evaluation Dataset

A centralized benchmark for Linguistic Code-switching Evaluation (LinCE) that combines ten corpora covering four different code-switched language pairs (i.e., Spanish-English, Nepali-English, Hindi-English, and Modern Standard Arabic-Egyptian Arabic) and four tasks (i.e., language identification, named entity recognition, part-of-speech tagging, and sentiment analysis). 

Source: [LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation](/paper/lince-a-centralized-benchmark-for-linguistic)

Introducing paper: LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation
Liputan6

A large-scale Indonesian summarization dataset consisting of harvested articles from Liputan6.com, an online news portal, resulting in 215,827 document-summary pairs.

Source: [Liputan6: A Large-scale Indonesian Dataset for Text Summarization](/paper/liputan6-a-large-scale-indonesian-dataset-for)

Introducing paper: Liputan6: A Large-scale Indonesian Dataset for Text Summarization
LISA Gaze Dataset

LISA Gaze is a dataset for driver gaze estimation comprising of 11 long drives, driven by 10 subjects in two different cars. 

Source: [Driver Gaze Zone Estimation using Convolutional Neural Networks: A General Framework and Ablative Analysis](/paper/driver-gaze-zone-estimation-using)

Introducing paper: Driver Gaze Zone Estimation using Convolutional Neural Networks: A General Framework and Ablative Analysis
Live Comment Dataset

The Live Comment Dataset is a large-scale dataset with 2,361 videos and 895,929 live comments that were written while the videos were streamed.

Source: [LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts](/paper/livebot-generating-live-video-comments-based)

Introducing paper: LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts
LiveQA

A new question answering dataset constructed from play-by-play live broadcast. It contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games, which are collected from the Chinese Hupu (https://nba.hupu.com/games) website.

Source: [LiveQA: A Question Answering Dataset over Sports Live](/paper/liveqa-a-question-answering-dataset-over)

Introducing paper: LiveQA: A Question Answering Dataset over Sports Live
LIVE-YT-HFR
Full name: LIVE YouTube High Frame Rate

LIVE-YT-HFR comprises of 480 videos having 6 different frame rates, obtained from 16 diverse contents.

Source: [Subjective and Objective Quality Assessment of High Frame Rate Videos](/paper/subjective-and-objective-quality-assessment-1)

Introducing paper: Subjective and Objective Quality Assessment of High Frame Rate Videos
LKS
Full name: Liver Kidney Stomach

**LKS** is a dataset of 684 Liver-Kidney-Stomach immunofluorescence whole slide images (WSIs) used in the investigation of autoimmune liver disease.

Source: [https://arxiv.org/abs/2003.05080](https://arxiv.org/abs/2003.05080)
Image Source: [https://github.com/cradleai/LKS-Dataset](https://github.com/cradleai/LKS-Dataset)

Introducing paper: SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image Classification
WI-LOCNESS
Full name: Cambridge English Write & Improve & LOCNESS

WI-LOCNESS is part of the [Building Educational Applications 2019 Shared Task for Grammatical Error Correction](https://www.cl.cam.ac.uk/research/nl/bea2019st/). It consists of two datasets:

- **LOCNESS**: is a corpus consisting of essays written by native English students. 
- **Cambridge English Write & Improve** (**W&I**): Write & Improve (Yannakoudakis et al., 2018) is an online web platform that assists non-native English students with their writing. Specifically, students from around the world submit letters, stories, articles and essays in response to various prompts, and the W&I system provides instant feedback. Since W&I went live in 2014, W&I annotators have manually annotated some of these submissions and assigned them a CEFR level.

Source: [The BEA-2019 Shared Task on Grammatical Error Correction](/paper/the-bea-2019-shared-task-on-grammatical-error)

Image source: [WI-LOCNESS](https://www.cl.cam.ac.uk/research/nl/bea2019st/#data)

Introducing paper: The BEA-2019 Shared Task on Grammatical Error Correction