Multi-Label Classification

The COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models. It is an essential dataset for researchers and developers working on object detection, segmentation, and pose estimation tasks.

The Medical Information Mart for Intensive Care III (MIMIC-III) dataset is a large, de-identified and publicly-available collection of medical records. Each record in the dataset includes ICD-9 codes, which identify diagnoses and procedures performed. Each code is partitioned into sub-codes, which often include specific circumstantial details. The dataset consists of 112,000 clinical reports records (average length 709.3 tokens) and 1,159 top-level ICD-9 codes. Each report is assigned to 7.6 codes, on average. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework. Source: MIT Laboratory for Computational Biology

The CheXpert dataset contains 224,316 chest radiographs of 65,240 patients with both frontal and lateral views available. The task is to do automated chest x-ray interpretation, featuring uncertainty labels and radiologist-labeled reference standard evaluation sets. Source: Deep Mining External Imperfect Data for Chest X-ray Disease Screening Image Source: https://stanfordmlgroup.github.io/competitions/chexpert/

The NUS-WIDE dataset contains 269,648 images with a total of 5,018 tags collected from Flickr. These images are manually annotated with 81 concepts, including objects and scenes. Source: Parallel Grid Pooling for Data Augmentation Image Source: Li et al

The RCV1 dataset is a benchmark dataset on text categorization. It is a collection of newswire articles producd by Reuters in 1996-1997. It contains 804,414 manually labeled newswire documents, and categorized with respect to three controlled vocabularies: industries, topics and regions. Source: Random Projections for Linear Support Vector Machines Image Source: https://www.nasdaq.com/publishers/reuters

The New York Times Annotated Corpus contains over 1.8 million articles written and published by the New York Times between January 1, 1987 and June 19, 2007 with article metadata provided by the New York Times Newsroom, the New York Times Indexing Service and the online production staff at nytimes.com. The corpus includes: - Over 1.8 million articles (excluding wire services articles that appeared during the covered period). - Over 650,000 article summaries written by library scientists. - Over 1,500,000 articles manually tagged by library scientists with tags drawn from a normalized indexing vocabulary of people, organizations, locations and topic descriptors. - Over 275,000 algorithmically-tagged articles that have been hand verified by the online production staff at nytimes.com. As part of the New York Times' indexing procedures, most articles are manually summarized and tagged by a staff of library scientists. This collection contains over 650,000 article-summary pairs which may prove to be useful in the development and evaluation of algorithms for automated document summarization. Also, over 1.5 million documents have at least one tag. Articles are tagged for persons, places, organizations, titles and topics using a controlled vocabulary that is applied consistently across articles. For instance if one article mentions "Bill Clinton" and another refers to "President William Jefferson Clinton", both articles will be tagged with "CLINTON, BILL". Source: https://catalog.ldc.upenn.edu/LDC2008T19

MIMIC-CXR from Massachusetts Institute of Technology presents 371,920 chest X-rays associated with 227,943 imaging studies from 65,079 patients. The studies were performed at Beth Israel Deaconess Medical Center in Boston, MA. Source: Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification Image Source: https://arxiv.org/abs/1901.07042

ChestX-ray14 is a medical imaging dataset which comprises 112,120 frontal-view X-ray images of 30,805 (collected from the year of 1992 to 2015) unique patients with the text-mined fourteen common disease labels, mined from the text radiological reports via NLP techniques. It expands on ChestX-ray8 by adding six additional thorax diseases: Edema, Emphysema, Fibrosis, Pleural Thickening and Hernia. Source: https://nihcc.app.box.com/v/ChestXray-NIHCC/file/220660789610 Image Source: https://nihcc.app.box.com/v/ChestXray-NIHCC

PASCAL VOC 2007 is a dataset for image recognition. The twenty object classes that have been selected are: Person: person Animal: bird, cat, cow, dog, horse, sheep Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor The dataset can be used for image classification and object detection tasks. Image Source: Object Detection and Recognition in Images

Web of Science (WOS) is a document classification dataset that contains 46,985 documents with 134 categories which include 7 parents categories. Source: HDLTex: Hierarchical Deep Learning for Text Classification

ECHR is an English legal judgment prediction dataset of cases from the European Court of Human Rights (ECHR). The dataset contains ~11.5k cases, including the raw text. For each case, the dataset provides a list of facts extracted using regular expressions from the case description. Each case is also mapped to articles of the Convention that were violated (if any). An importance score is also assigned by ECHR. Source: Neural Legal Judgment Prediction in English

The friedman1 data set is commonly used to test semi-supervised regression methods. Source: http://search.r-project.org/library/ssr/html/friedman1.html

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

EURLEX57K is a new publicly available legal LMTC dataset, dubbed EURLEX57K, containing 57k English EU legislative documents from the EUR-LEX portal, tagged with ∼4.3k labels (concepts) from the European Vocabulary (EUROVOC). Source: Large-Scale Multi-Label Text Classification on EU Legislation

OpenImages V6 is a large-scale dataset , consists of 9 million training images, 41,620 validation samples, and 125,456 test samples. It is a partially annotated dataset, with 9,600 trainable classes

Our goal is to improve upon the status quo for designing image classification models trained in one domain that perform well on images from another domain. Complementing existing work in robustness testing, we introduce the first test dataset for this purpose which comes from an authentic use case where photographers wanted to learn about the content in their images. We built a new test set using 8,900 images taken by people who are blind for which we collected metadata to indicate the presence versus absence of 200 ImageNet object categories. We call this dataset VizWiz-Classification.

CAL500 (Computer Audition Lab 500) is a dataset aimed for evaluation of music information retrieval systems. It consists of 502 songs picked from western popular music. The audio is represented as a time series of the first 13 Mel-frequency cepstral coefficients (and their first and second derivatives) extracted by sliding a 12 ms half-overlapping short-time window over the waveform of each song. Each song has been annotated by at least 3 people with 135 musically-relevant concepts spanning six semantic categories: * 29 instruments were annotated as present in the song or not, * 22 vocal characteristics were annotated as relevant to the singer or not, * 36 genres, * 18 emotions were rated on a scale from one to three (e.g., not happy", neutral", ``happy"), * 15 song concepts describing the acoustic qualities of the song, artist and recording (e.g., tempo, energy, sound quality), * 15 usage terms (e.g., "I would listen to this song while driving, sleeping, etc."). Source: http://calab1.ucsd.edu/~datasets/cal500/details_cal500.txt Audio Source: http://calab1.ucsd.edu/~datasets/cal500/cal500data/

The objective in extreme multi-label classification is to learn feature architectures and classifiers that can automatically tag a data point with the most relevant subset of labels from an extremely large label set. This repository provides resources that can be used for evaluating the performance of extreme multi-label algorithms including datasets, code, and metrics. For more details please visit the link http://manikvarma.org/downloads/XC/XMLRepository.html

LSHTC is a dataset for large-scale text classification. The data used in the LSHTC challenges originates from two popular sources: the DBpedia and the ODP (Open Directory Project) directory, also known as DMOZ. DBpedia instances were selected from the english, non-regional Extended Abstracts provided by the DBpedia site. The DMOZ instances consist of either Content vectors, Description vectors or both. A Content vectors is obtained by directly indexing the web page using standard indexing chain (preprocessing, stemming/lemmatization, stop-word removal). Source: LSHTC: A Benchmark for Large-Scale Text Classification

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 Image Source: Qi et al

Arxiv ASTRO-PH (Astro Physics) collaboration network is from the e-print arXiv and covers scientific collaborations between authors papers submitted to Astro Physics category. If an author i co-authored a paper with author j, the graph contains a undirected edge from i to j. If the paper is co-authored by k authors this generates a completely connected (sub)graph on k nodes. Source: https://snap.stanford.edu/data/ca-AstroPh.html

Moviescope is a large-scale dataset of 5,000 movies with corresponding video trailers, posters, plots and metadata. Moviescope is based on the IMDB 5000 dataset consisting of 5.043 movie records. It is augmented by crawling video trailers associated with each movie from YouTube and text plots from Wikipedia. Source: Moviescope: Large-scale Analysis of Movies using Multiple Modalities

CHiME-Home is a dataset for sound source recognition in a domestic environment. It uses around 6.8 hours of domestic environment audio recordings. The recordings were obtained from the CHiME projects – computational hearing in multisource environments – where recording equipment was positioned inside an English Victorian semi-detached house. The recordings were selected from 22 sessions totalling 19.5 hours, with each session made between 7:30 in the morning and 20:00 in the evening. In the considered recordings, the equipment was placed in the lounge (sitting room) near the door opening onto a hallway, with the hallway opening onto a kitchen with no door. With the lounge door typically open, prominent sounds thus may originate from sources both in the lounge and kitchen. The choice of permitted labels was motivated by the sources present in the considered acoustic environment: Human speakers (c,m,f); human activity (p); television (v); household appliances (b). Further labels o,S,U respectively relate to any other identifiable sounds, silence, unidentifiable sounds. Labels S,U may respectively only be assigned in isolation. Annotators were acquired to assign at least one label to a chunk, thus annotators may either assign one or more labels from the set {c,m,f,v,p,b,o}, or may alternatively ‘flag’ the chunk using a single label from the set {S,U}. Source: https://www.researchgate.net/publication/308732345_CHiME-Home_A_dataset_for_sound_source_recognition_in_a_domestic_environment Image Source: https://www.researchgate.net/publication/308732345_CHiME-Home_A_dataset_for_sound_source_recognition_in_a_domestic_environment

Sewer-ML is a sewer defect dataset. It contains 1.3 million images, from 75,618 videos collected from three Danish water utility companies over nine years. All videos have been annotated by licensed sewer inspectors following the Danish sewer inspection standard, Fotomanualen. This leads to consistent and reliable annotations, and a total of 17 annotated defect classes. Source: Sewer-ML Image Source: Sewer-ML

As part of an ongoing worldwide effort to comprehend and monitor insect biodiversity, we present the BIOSCAN-5M Insect dataset to the machine learning community. BIOSCAN-5M is a comprehensive dataset containing multi-modal information for over 5 million insect specimens, and it significantly expands existing image-based biological datasets by including taxonomic labels, raw nucleotide barcode sequences, assigned barcode index numbers, geographical information, and specimen size. Every record has both image and DNA data. Each record of the BIOSCAN-5M dataset contains six primary attributes: RGB image DNA barcode sequence Barcode Index Number (BIN) Biological taxonomic classification Geographical information Specimen size

For each dataset we provide a short description as well as some characterization metrics. It includes the number of instances (m), number of attributes (d), number of labels (q), cardinality (Card), density (Dens), diversity (Div), average Imbalance Ratio per label (avgIR), ratio of unconditionally dependent label pairs by chi-square test (rDep) and complexity, defined as m × q × d as in [Read 2010]. Cardinality measures the average number of labels associated with each instance, and density is defined as cardinality divided by the number of labels. Diversity represents the percentage of labelsets present in the dataset divided by the number of possible labelsets. The avgIR measures the average degree of imbalance of all labels, the greater avgIR, the greater the imbalance of the dataset. Finally, rDep measures the proportion of pairs of labels that are dependent at 99% confidence. A broader description of all the characterization metrics and the used partition methods are described in the MLDA documentation. We also used MLDA for the characterization and partitioning of the datasets.

Aims to help V-NLIs recognize analytic tasks from free-form natural language by training and evaluating cutting-edge multi-label classification models. The dataset contains diverse user queries, and each is annotated with one or multiple analytic tasks. Source: Quda: Natural Language Queries for Visual Data Analytics

This dataset contains Bangla handwritten numerals, basic characters and compound characters. This dataset was collected from multiple geographical location within Bangladesh and includes sample collected from a variety of aged groups. This dataset can also be used for other classification problems i.e: gender, age, district. Source: BanglaLekha-Isolated: A Comprehensive Bangla Handwritten Character Dataset

MIMIC-IV ICD-10 contains 122,279 discharge summaries—free-text medical documents—annotated with ICD-10 diagnosis and procedure codes. It contains data for patients admitted to the Beth Israel Deaconess Medical Center emergency department or ICU between 2008-2019. All codes with fewer than ten examples have been removed, and the train-val-test split was created using [multi-label stratified sampling](MIMIC-IV ICD-9). The dataset is described further in Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study, and the code to use the dataset is found here. The dataset is intended for medical code prediction and was created using MIMIC-IV v2.2 and MIMIC-IV-NOTE v2.2. Using the two datasets requires a license obtained in Physionet; this can take a couple of days.

The MIMIC-IV-ICD10-full dataset, including occurring labels.

The MIMIC-IV-ICD9 dataset, including all occurring labels.

CAVES is the first large-scale dataset containing about 10k COVID-19 anti-vaccine tweets labelled into various specific anti-vaccine concerns in a multi-label setting. This is also the first multi-label classification dataset that provides explanations for each of the labels. Additionally, the dataset also provides class-wise summaries of all the tweets.

Hierarchical multi-label classification dataset for functional genomics

Hierarchical-multilabel classification dataset for functional genomics

Hierarchical-multilabel classification dataset for functional genomics

Hierarchical-multilabel classification dataset for functional genomics