Omkar2118/datasets huggingface dataset:
MinneApple

**MinneApple** is a benchmark dataset for apple detection and segmentation. The fruits are labelled using polygonal masks for each object instance to aid in precise object detection, localization, and segmentation. Additionally, the dataset also contains data for patch-based counting of clustered fruits. The dataset contains over 41, 000 annotated object instances in 1000 images.

Source: [https://github.com/nicolaihaeni/MinneApple](https://github.com/nicolaihaeni/MinneApple)
Image Source: [https://github.com/nicolaihaeni/MinneApple](https://github.com/nicolaihaeni/MinneApple)

Introducing paper: MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
MitoEM

Contains mitochondria instances.

Source: [MitoEM](https://mitoem.grand-challenge.org/)
MITOS_WSI_CMC

A dataset of 21 WSIs of CMC completely annotated for MF. For this, a pathologist screened all WSIs for potential MF and structures with a similar appearance.

Source: [A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research](/paper/dogs-as-model-for-human-breast-cancer-a)

Introducing paper: A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research
MIZAN

Persian-English parallel corpus with more than one million sentence pairs collected from masterpieces of literature.

Source: [MIZAN: A Large Persian-English Parallel Corpus](/paper/mizan-a-large-persian-english-parallel-corpus)

Introducing paper: MIZAN: A Large Persian-English Parallel Corpus
MJU-Waste

**MJU-Waste** is an RGBD waste object segmentation dataset that is made public to facilitate future research in this area.

Source: [https://github.com/realwecan/mju-waste](https://github.com/realwecan/mju-waste)
Image Source: [Wang et al](https://arxiv.org/pdf/2007.04259v1.pdf)

Introducing paper: A Multi-Level Approach to Waste Object Segmentation
MKQA
Full name: Multilingual Knowledge Questions and Answers

Multilingual Knowledge Questions and Answers (MKQA) is an open-domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages (260k question-answer pairs in total). The goal of this dataset is to provide a challenging benchmark for question answering quality across a wide set of languages. Answers are based on a language-independent data representation, making results comparable across languages and independent of language-specific passages. With 26 languages, this dataset supplies the widest range of languages to-date for evaluating question answering.

Introducing paper: MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering
MK-SQuIT

An example dataset of 110,000 question/query pairs across four WikiData domains.

Source: [MK-SQuIT: Synthesizing Questions using Iterative Template-filling](/paper/mk-squit-synthesizing-questions-using)

Introducing paper: MK-SQuIT: Synthesizing Questions using Iterative Template-filling
MLB Dataset

A new dataset on the baseball domain.

Source: [Data-to-text Generation with Entity Modeling](/paper/data-to-text-generation-with-entity-modeling)

Introducing paper: Data-to-text Generation with Entity Modeling
MLB-YouTube Dataset

The MLB-YouTube dataset is a new, large-scale dataset consisting of 20 baseball games from the 2017 MLB post-season available on YouTube with over 42 hours of video footage. The dataset consists of two components: segmented videos for activity recognition and continuous videos for activity classification. It is quite challenging as it is created from TV broadcast baseball games where multiple different activities share the camera angle. Further, the motion/appearance difference between the various activities is quite small.

Source: [https://github.com/piergiaj/mlb-youtube](https://github.com/piergiaj/mlb-youtube)
Image Source: [https://github.com/piergiaj/mlb-youtube](https://github.com/piergiaj/mlb-youtube)

Introducing paper: Fine-grained Activity Recognition in Baseball Videos
MLM

A new resource to train and evaluate multitask systems on samples in multiple modalities and three languages. 

Source: [MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities](/paper/mlm-a-benchmark-dataset-for-multitask)

Introducing paper: MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities
MLMA Hate Speech

A new multilingual multi-aspect hate speech analysis dataset and use it to test the current state-of-the-art multilingual multitask learning approaches.

Source: [Multilingual and Multi-Aspect Hate Speech Analysis](/paper/multilingual-and-multi-aspect-hate-speech)

Introducing paper: Multilingual and Multi-Aspect Hate Speech Analysis
MLQE
Full name: MultiLingual Quality Estimation

The **MLQE** dataset is a dataset for sentence-level Machine Translation Quality Estimation. It consists of 6 language pairs representing NMT training in high, medium, and low-resource scenarios. The corpus is extracted from Wikipedia, and 10K segments per language pair are annotated.

Source: [https://github.com/facebookresearch/mlqe](https://github.com/facebookresearch/mlqe)

Introducing paper: Unsupervised Quality Estimation for Neural Machine Translation
MLQE-PE
Full name: Multilingual Quality Estimation and Automatic Post-editing Dataset

The Multilingual Quality Estimation and Automatic Post-editing (**MLQE-PE**) Dataset is a dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE). The dataset contains seven language pairs, with human labels for 9,000 translations per language pair in the following formats: sentence-level direct assessments and post-editing effort, and word-level good/bad labels. It also contains the post-edited sentences, as well as titles of the articles where the sentences were extracted from, and the neural MT models used to translate the text.

Source: [https://github.com/sheffieldnlp/mlqe-pe](https://github.com/sheffieldnlp/mlqe-pe)

Introducing paper: MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset
MLRSNet

**MLRSNet** is a a multi-label high spatial resolution remote sensing dataset for semantic scene understanding. It provides different perspectives of the world captured from satellites. That is, it is composed of high spatial resolution optical satellite images. MLRSNet contains 109,161 remote sensing images that are annotated into 46 categories, and the number of sample images in a category varies from 1,500 to 3,000. The images have a fixed size of 256×256 pixels with various pixel resolutions (~10m to 0.1m). Moreover, each image in the dataset is tagged with several of 60 predefined class labels, and the number of labels associated with each image varies from 1 to 13. The dataset can be used for multi-label based image classification, multi-label based image retrieval, and image segmentation.

Source: [https://github.com/cugbrs/MLRSNet](https://github.com/cugbrs/MLRSNet)
Image Source: [Qi et al](https://arxiv.org/pdf/2010.00243v1.pdf)

Introducing paper: MLRSNet: A Multi-label High Spatial Resolution Remote Sensing Dataset for Semantic Scene Understanding
MLS
Full name: Multiple Light Source

The **Multiple Light Source** dataset (**MLS**) is a collection of 24 multiple object scenes each recorded under 18 multiple light source illumination scenarios. The illuminants are varying in dominant spectral colours, intensity and distance from the scene. The dataset can be used for the evaluation of computational colour constancy algorithms. Along with the images of the scenes the spectral characteristics of the camera, light sources and the objects are also provided, and each image includes pixel-by-pixel ground truth annotation of uniformly coloured object surfaces thus making this useful for benchmarking colour-based image segmentation algorithms.

Source: [https://arxiv.org/abs/1908.06126](https://arxiv.org/abs/1908.06126)
Image Source: [https://github.com/Visillect/mls-dataset](https://github.com/Visillect/mls-dataset)

Introducing paper: Multiple Light Source Dataset for Colour Research
MLSUM
Full name: MultiLingual SUMmarization

A large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community.

Source: [MLSUM: The Multilingual Summarization Corpus](/paper/mlsum-the-multilingual-summarization-corpus)

Introducing paper: MLSUM: The Multilingual Summarization Corpus
MMAct

MMAct is a large-scale dataset for multi/cross modal action understanding. This dataset has been recorded from 20 distinct subjects with seven different types of modalities: RGB videos, keypoints, acceleration, gyroscope, orientation, Wi-Fi and pressure signal. The dataset consists of more than 36k video clips for 37 action classes covering a wide range of daily life activities such as desktop-related and check-in-based ones in four different distinct scenarios. 

Source: [MMAct: A Large-Scale Dataset for Cross Modal Human Action Understanding](/paper/mmact-a-large-scale-dataset-for-cross-modal)

Introducing paper: MMAct: A Large-Scale Dataset for Cross Modal Human Action Understanding
MMD
Full name: Multimodal Dialogs

The MMD (MultiModal Dialogs) dataset is a dataset for multimodal domain-aware conversations. It consists of over 150K conversation sessions between shoppers and sales agents, annotated by a group of in-house annotators using a semi-automated manually intense iterative process. 

Source: [Towards Building Large Scale Multimodal Domain-Aware Conversation Systems](/paper/towards-building-large-scale-multimodal)

Introducing paper: Towards Building Large Scale Multimodal Domain-Aware Conversation Systems
MMED

Contains 25,165 textual news articles collected from hundreds of news media sites (e.g., Yahoo News, Google News, CNN News.) and 76,516 image posts shared on Flickr social media, which are annotated according to 412 real-world events. The dataset is collected to explore the problem of organizing heterogeneous data contributed by professionals and amateurs in different data domains, and the problem of transferring event knowledge obtained from one data domain to heterogeneous data domain, thus summarizing the data with different contributors.

Source: [MMED: A Multi-domain and Multi-modality Event Dataset](/paper/mmed-a-multi-domain-and-multi-modality-event)

Introducing paper: MMED: A Multi-domain and Multi-modality Event Dataset
MMID
Full name: Massively Multilingual Image Dataset

A large-scale multilingual corpus of images, each labeled with the word it represents. The dataset includes approximately 10,000 words in each of 100 languages.

Source: [Learning Translations via Images with a Massively Multilingual Image Dataset](/paper/learning-translations-via-images-with-a)

Introducing paper: Learning Translations via Images with a Massively Multilingual Image Dataset
MNIST-1D

A minimalist, low-memory, and low-compute alternative to classic deep learning benchmarks. The training examples are 20 times smaller than MNIST examples yet they differentiate more clearly between linear, nonlinear, and convolutional models which attain 32, 68, and 94% accuracy respectively (these models obtain 94, 99+, and 99+% on MNIST).

Source: [Scaling down Deep Learning](/paper/scaling-down-deep-learning)

Introducing paper: Scaling Down Deep Learning with MNIST-1D
MNIST-MIX

**MNIST-MIX** is a multi-language handwritten digit recognition dataset. It contains digits from 10 different languages.

Source: [https://github.com/jwwthu/MNIST-MIX](https://github.com/jwwthu/MNIST-MIX)

Introducing paper: MNIST-MIX: A Multi-language Handwritten Digit Recognition Dataset
Mo2Cap2

A large ground truth training corpus of top-down fisheye images.

Source: [Mo2Cap2](http://gvv.mpi-inf.mpg.de/projects/wxu/Mo2Cap2/)
MobiBits
Full name: Multimodal Mobile Biometric Database

A novel database comprising representations of five different biometric characteristics, collected in a mobile, unconstrained or semi-constrained setting with three different mobile devices, including characteristics previously unavailable in existing datasets, namely hand images, thermal hand images, and thermal face images, all acquired with a mobile, off-the-shelf device.

Source: [MobiBits: Multimodal Mobile Biometric Database](/paper/mobibits-multimodal-mobile-biometric-database)

Introducing paper: MobiBits: Multimodal Mobile Biometric Database
MOCHA

Contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. 

Source: [MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics](/paper/mocha-a-dataset-for-training-and-evaluating)

Introducing paper: MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics
MOD++

Includes challenging sequences and extensive data stratification in-terms of camera and object motion, velocity magnitudes, direction, and rotational speeds.

Source: [0-MMS: Zero-Shot Multi-Motion Segmentation With A Monocular Event Camera](/paper/moms-with-events-multi-object-motion)

Introducing paper: 0-MMS: Zero-Shot Multi-Motion Segmentation With A Monocular Event Camera
Modern Hebrew Sentiment Dataset

Modern Hebrew Sentiment Dataset is a sentiment analysis benchmark for Hebrew, based on 12K social media comments, and provide two instances of these data: in token-based and morpheme-based settings.

Source: [Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from Modern Hebrew](/paper/representations-and-architectures-in-neural)

Introducing paper: Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from Modern Hebrew
Molweni

A machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog. Molweni's source samples from the Ubuntu Chat Corpus, including 10,000 dialogs comprising 88,303 utterances. 

Source: [Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure](/paper/molweni-a-challenge-multiparty-dialogues)

Introducing paper: Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure
MonoPerfCap Dataset

MonoPerfCap is a benchmark dataset for human 3D performance capture from monocular video input  consisting of around 40k frames, which covers a variety of different scenarios.

Introducing paper: MonoPerfCap: Human Performance Capture from Monocular Video