19,997 machine learning datasets
19,997 dataset results
The bipedal skills benchmark is a suite of reinforcement learning environments implemented for the MuJoCo physics simulator. It aims to provide a set of tasks that demand a variety of motor skills beyond locomotion, and is intended for evaluating skill discovery and hierarchical learning methods. The majority of tasks exhibit a sparse reward structure.
Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems. Identifying vessels that do not show up in conventional monitoring systems---known as ``dark vessels''---is key to managing and securing the health of marine environments. With the rise of satellite-based synthetic aperture radar (SAR) imaging and modern machine learning (ML), it is now possible to automate detection of dark vessels day or night, under all-weather conditions. SAR images, however, require a domain-specific treatment and are not widely accessible to the ML community. Maritime objects (vessels and offshore infrastructure) are relatively small and sparse, challenging traditional computer vision approaches. We present the largest labeled dataset for training ML models to detect and characterize vessels and ocean structures in SAR imagery. xView3-SAR consists of nearly 1,000 analysis-ready SAR images from the Sentinel-1 mission that are, on average, 29,400-by-24,400 pixels each.
Timely and effective response to humanitarian crises requires quick and accurate analysis of large amounts of text data, a process that can highly benefit from expert-assisted NLP systems trained on validated and annotated data in the humanitarian response domain. To enable creation of such NLP systems, we introduce and release HumSet, a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. The dataset provides documents in three languages (English, French, Spanish) and covers a variety of humanitarian crises from 2018 to 2021 across the globe. For each document, HumSet provides selected snippets (entries) as well as assigned classes to each entry annotated using common humanitarian information analysis frameworks. HumSet also provides novel and challenging entry extraction and multi-label entry classification tasks. In this paper, we take a first step towards approaching these tasks and conduct a set of expe
CLSE is an augmented version of the Schema-Guided Dialog Dataset. The corpus includes 34 languages and covers 74 different semantic types to support various applications from airline ticketing to video games.
Paper2Fig100k is a dataset with over 100k images of figures and texts from research papers. The figures show architecture diagrams and methodologies of articles available at arXiv.org from fields like artificial intelligence and computer vision. Figures usually include text and discrete objects, e.g., boxes in a diagram, with lines and arrows that connect them.
CRIPP-VQA is a video question answering dataset for reasoning about the implicit physical properties of objects in a scene. It contains videos of object in motion, annotated with questions that involve counterfactual reasoning about actions, questions about planning in order to reach a goal, and descriptive questions about visible properties of objects.
Amharic - English Parallel Corpus for Machine Translation contains 33,955 sentence pairs extracted text from such news platforms as Ethiopian Press Agency1, Fana Broadcasting Corporate2, and Walta Information Center3. As the data we used is from different sources, it includes various domains such as religious (Bible and Quran), politics, economics, sports, news, among others.
Concise has two datasets of 2000 sentences each, that were annotated by two and five human annotators, respectively. They are designed for the new task of making sentence concise.
DIALOCONAN is a dataset comprising over 3000 fictitious multi-turn dialogues between a hater and an NGO operator, covering 6 targets of hate.
SciHTC is a dataset for hierarchical multi-label text classification (HMLTC) of scientific papers which contains 186,160 papers and 1,233 categories from the ACM CCS tree.
ToM-in-AMC is a novel NLP benchmark, Short for Theory-of-Mind meta-learning Assessment with Movie Characters. The benchmark consists of 1,000 parsed movie scripts for this purpose, each corresponding to a few-shot character understanding task.
The QV-Pipe dataset consists of 9.6k videos, which are collected from real-world urban pipes. The total duration of all videos exceeds 55 hours. Moreover, there are 1 normal class and 16 defect classes. Because the pipe situation is complex and multiple defects often appear at the same time, each video is annotated with multiple labels.
Our CCTV-Pipe dataset consists of 16 defect categories including structural and functional defects in the pipe. It contains 575 videos with 87 hours, which are collected from real-world urban pipe systems. Different from traditional temporal action localization, our goal in this realistic scenario is to find preferable temporal locations of defects from a untrimmed CCTV video, instead of exact temporal boundaries.
This collection contains images from 422 non-small cell lung cancer (NSCLC) patients. For these patients pretreatment CT scans, manual delineation by a radiation oncologist of the 3D volume of the gross tumor volume and clinical outcome data are available.
MedleyVox is an evaluation dataset for multiple singing voices separation that corresponds to such categories. The problem definition in this dataset is categorised into i) duet, ii) unison, iii) main vs. rest, and iv) N-singing separation.
Factual Inconsistency Benchmark (FIB) is a benchmark that focuses on the task of summarization. Specifically, the benchmark involves comparing the scores an LLM assigns to a factually consistent versus a factual inconsistent summary for an input news article. For factually consistent summaries, human-written reference summaries are used to manually verify as factually consistent.
EmoPars is a dataset of 30,000 Persian Tweets labeled with Ekman’s six basic emotions (Anger, Fear, Happiness, Sadness, Hatred, and Wonder). This is the first publicly available emotion dataset in the Persian language.
SSL4EO-S12 is a large-scale, global, multimodal, and multi-seasonal corpus of satellite imagery from the ESA Sentinel-1 & -2 satellite missions.
Chinese Spelling Correction Dataset for errors generated by pinyin IME (CSCD-IME), a dataset containing 40,000 annotated sentences from real posts of official media on Sina Weibo. It is designed to detect and correct spelling mistakes in Chinese texts.
We provide a database containing shot scale annotations (i.e., the apparent distance of the camera from the subject of a filmed scene) for more than 792,000 image frames. Frames belong to 124 full movies from the entire filmographies by 6 important directors: Martin Scorsese, Jean-Luc Godard, Béla Tarr, Federico Fellini, Michelangelo Antonioni, and Ingmar Bergman. Each frame, extracted from videos at 1 frame per second, is annotated on the following scale categories: Extreme Close Up (ECU), Close Up (CU), Medium Close Up (MCU), Medium Shot (MS), Medium Long Shot (MLS), Long Shot (LS), Extreme Long Shot (ELS), Foreground Shot (FS), and Insert Shots (IS). Two independent coders annotated all frames from the 124 movies, whilst a third one checked their coding and made decisions in cases of disagreement. The CineScale database enables AI-driven interpretation of shot scale data and opens to a large set of research activities related to the automatic visual analysis of cinematic material, s