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The Lip Reading in the Wild (LRW) dataset a large-scale audio-visual database that contains 500 different words from over 1,000 speakers. Each utterance has 29 frames, whose boundary is centered around the target word. The database is divided into training, validation and test sets. The training set contains at least 800 utterances for each class while the validation and test sets contain 50 utterances. Source: Towards Pose-invariant Lip-Reading Image Source: https://www.robots.ox.ac.uk/~vgg/data/lip_reading/lrw1.html

Celeb-DF is a large-scale challenging dataset for deepfake forensics. It includes 590 original videos collected from YouTube with subjects of different ages, ethnic groups and genders, and 5639 corresponding DeepFake videos. Source: https://github.com/danmohaha/celeb-deepfakeforensics Image Source: https://github.com/danmohaha/celeb-deepfakeforensics

The CoNLL dataset is a widely used resource in the field of natural language processing (NLP). The term “CoNLL” stands for Conference on Natural Language Learning. It originates from a series of shared tasks organized at the Conferences of Natural Language Learning.

RESISC45 dataset is a dataset for Remote Sensing Image Scene Classification (RESISC). It contains 31,500 RGB images of size 256×256 divided into 45 scene classes, each class containing 700 images. Among its notable features, RESISC45 contains varying spatial resolution ranging from 20cm to more than 30m/px.

The Schema-Guided Dialogue (SGD) dataset consists of over 20k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 20 domains, ranging from banks and events to media, calendar, travel, and weather. For most of these domains, the dataset contains multiple different APIs, many of which have overlapping functionalities but different interfaces, which reflects common real-world scenarios. The wide range of available annotations can be used for intent prediction, slot filling, dialogue state tracking, policy imitation learning, language generation, user simulation learning, among other tasks in large-scale virtual assistants. Besides these, the dataset has unseen domains and services in the evaluation set to quantify the performance in zero-shot or few shot settings. Source: The Schema-Guided Dialogue Dataset

SUNCG is a large-scale dataset of synthetic 3D scenes with dense volumetric annotations. The dataset is currently not available. Source: https://sscnet.cs.princeton.edu/ Image Source: https://sscnet.cs.princeton.edu/

Argoverse 2 (AV2) is a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions be- tween the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for “scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry — sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.

The Extended Yale B database contains 2414 frontal-face images with size 192×168 over 38 subjects and about 64 images per subject. The images were captured under different lighting conditions and various facial expressions. Source: Learning Locality-Constrained Collaborative Representation for Robust Face Recognition Image Source: http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html

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A new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Source: Aligning AI With Shared Human Values

SA-1B consists of 11M diverse, high resolution, licensed, and privacy protecting images and 1.1B high-quality segmentation masks. Source: Segment Anything Image Source: Segment Anything

The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For the majority of the questions, an additional paragraph with supporting evidence for the correct answer is provided. Source: Allen Institute for AI

OTB-2015, also referred as Visual Tracker Benchmark, is a visual tracking dataset. It contains 100 commonly used video sequences for evaluating visual tracking. Image Source: http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html

MARS (Motion Analysis and Re-identification Set) is a large scale video based person reidentification dataset, an extension of the Market-1501 dataset. It has been collected from six near-synchronized cameras. It consists of 1,261 different pedestrians, who are captured by at least 2 cameras. The variations in poses, colors and illuminations of pedestrians, as well as the poor image quality, make it very difficult to yield high matching accuracy. Moreover, the dataset contains 3,248 distractors in order to make it more realistic. Deformable Part Model and GMMCP tracker were used to automatically generate the tracklets (mostly 25-50 frames long). Source: Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets

The APPS dataset consists of problems collected from different open-access coding websites such as Codeforces, Kattis, and more. The APPS benchmark attempts to mirror how humans programmers are evaluated by posing coding problems in unrestricted natural language and evaluating the correctness of solutions. The problems range in difficulty from introductory to collegiate competition level and measure coding ability as well as problem-solving. The Automated Programming Progress Standard, abbreviated APPS, consists of 10,000 coding problems in total, with 131,836 test cases for checking solutions and 232,444 ground-truth solutions written by humans. Problems can be complicated, as the average length of a problem is 293.2 words. The data are split evenly into training and test sets, with 5,000 problems each. In the test set, every problem has multiple test cases, and the average number of test cases is 21.2. Each test case is specifically designed for the corresponding problem, enabling us to rigorously evaluate program functionality. Source: Measuring Coding Challenge Competence With APPS Image source: Measuring Coding Challenge Competence With APPS

HICO-DET is a dataset for detecting human-object interactions (HOI) in images. It contains 47,776 images (38,118 in train set and 9,658 in test set), 600 HOI categories constructed by 80 object categories and 117 verb classes. HICO-DET provides more than 150k annotated human-object pairs. V-COCO provides 10,346 images (2,533 for training, 2,867 for validating and 4,946 for testing) and 16,199 person instances. Each person has annotations for 29 action categories and there are no interaction labels including objects. Source: Visual Compositional Learning for Human-Object Interaction Detection Image Source: http://www-personal.umich.edu/~ywchao/hico/

Dataset of hate speech annotated on Internet forum posts in English at sentence-level. The source forum in Stormfront, a large online community of white nacionalists. A total of 10,568 sentence have been been extracted from Stormfront and classified as conveying hate speech or not. Source: https://arxiv.org/pdf/1809.04444.pdf

This dataset evaluates instruction following ability of large language models. There are 500+ prompts with instructions such as "write an article with more than 800 words", "wrap your response with double quotation marks", etc.

Long-range arena (LRA) is an effort toward systematic evaluation of efficient transformer models. The project aims at establishing benchmark tasks/datasets using which we can evaluate transformer-based models in a systematic way, by assessing their generalization power, computational efficiency, memory foot-print, etc. Long-Range Arena is specifically focused on evaluating model quality under long-context scenarios. The benchmark is a suite of tasks consisting of sequences ranging from 1K to 16K tokens, encompassing a wide range of data types and modalities such as text, natural, synthetic images, and mathematical expressions requiring similarity, structural, and visual-spatial reasoning. Description from: Long Range Arena : A Benchmark for Efficient Transformers

MORPH is a facial age estimation dataset, which contains 55,134 facial images of 13,617 subjects ranging from 16 to 77 years old. Source: Deep Ordinal Regression Forests Image Source: https://uncw.edu/oic/tech/morph.html

ShapeNetCore is a subset of the full ShapeNet dataset with single clean 3D models and manually verified category and alignment annotations. It covers 55 common object categories with about 51,300 unique 3D models. The 12 object categories of PASCAL 3D+, a popular computer vision 3D benchmark dataset, are all covered by ShapeNetCore.

The Breakfast Actions Dataset comprises of 10 actions related to breakfast preparation, performed by 52 different individuals in 18 different kitchens. The dataset is one of the largest fully annotated datasets available. The actions are recorded “in the wild” as opposed to a single controlled lab environment. It consists of over 77 hours of video recordings. Source: https://serre-lab.clps.brown.edu/resource/breakfast-actions-dataset/ Image Source: https://serre-lab.clps.brown.edu/resource/breakfast-actions-dataset/

Form Understanding in Noisy Scanned Documents (FUNSD) comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking. Source: FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents Image source: https://guillaumejaume.github.io/FUNSD/

Visual Commonsense Reasoning (VCR) is a large-scale dataset for cognition-level visual understanding. Given a challenging question about an image, machines need to present two sub-tasks: answer correctly and provide a rationale justifying its answer. The VCR dataset contains over 212K (training), 26K (validation) and 25K (testing) questions, answers and rationales derived from 110K movie scenes. Source: Visual Commonsense R-CNN Image Source: From Recognition to Cognition: Visual Commonsense Reasoning

The WebVision dataset is designed to facilitate the research on learning visual representation from noisy web data. It is a large scale web images dataset that contains more than 2.4 million of images crawled from the Flickr website and Google Images search. The same 1,000 concepts as the ILSVRC 2012 dataset are used for querying images, such that a bunch of existing approaches can be directly investigated and compared to the models trained from the ILSVRC 2012 dataset, and also makes it possible to study the dataset bias issue in the large scale scenario. The textual information accompanied with those images (e.g., caption, user tags, or description) are also provided as additional meta information. A validation set contains 50,000 images (50 images per category) is provided to facilitate the algorithmic development.

Builds on top of recent data collection efforts by domain experts in these applications and provides a unified collection of datasets with evaluation metrics and train/test splits that are representative of real-world distribution shifts. The v2.0 update adds unlabeled data to 8 datasets. The labeled data and evaluation metrics are exactly the same, so all previous results are directly comparable. Source: WILDS: A Benchmark of in-the-Wild Distribution Shifts

The AI2’s Reasoning Challenge (ARC) dataset is a multiple-choice question-answering dataset, containing questions from science exams from grade 3 to grade 9. The dataset is split in two partitions: Easy and Challenge, where the latter partition contains the more difficult questions that require reasoning. Most of the questions have 4 answer choices, with <1% of all the questions having either 3 or 5 answer choices. ARC includes a supporting KB of 14.3M unstructured text passages. Source: Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering Image Source: https://arxiv.org/abs/1803.05457

LabelMe database is a large collection of images with ground truth labels for object detection and recognition. The annotations come from two different sources, including the LabelMe online annotation tool. Source: LabelMe: A Database and Web-Based Tool for Image Annotation Image Source: Russell et al

Question Answering in Context is a large-scale dataset that consists of around 14K crowdsourced Question Answering dialogs with 98K question-answer pairs in total. Data instances consist of an interactive dialog between two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts (spans) from the text. Source: https://paperswithcode.com/paper/quac-question-answering-in-context-1/ Image Source: https://paperswithcode.com/paper/quac-question-answering-in-context-1/

WMT 2016 is a collection of datasets used in shared tasks of the First Conference on Machine Translation. The conference builds on ten previous Workshops on statistical Machine Translation. The conference featured ten shared tasks: - a news translation task, - an IT domain translation task, - a biomedical translation task, - an automatic post-editing task, - a metrics task (assess MT quality given reference translation). - a quality estimation task (assess MT quality without access to any reference), - a tuning task (optimize a given MT system), - a pronoun translation task, - a bilingual document alignment task, - a multimodal translation task. Source: http://www.statmt.org/wmt16/index.html

The Hateful Memes data set is a multimodal dataset for hateful meme detection (image + text) that contains 10,000+ new multimodal examples created by Facebook AI. Images were licensed from Getty Images so that researchers can use the data set to support their work.

NELL is a dataset built from the Web via an intelligent agent called Never-Ending Language Learner. This agent attempts to learn over time to read the web. NELL has accumulated over 50 million candidate beliefs by reading the web, and it is considering these at different levels of confidence. NELL has high confidence in 2,810,379 of these beliefs. Source: A Survey on Knowledge Graphs: Representation, Acquisition and Applications Image Source: http://rtw.ml.cmu.edu/rtw/

PASCAL-5i is a dataset used to evaluate few-shot segmentation. The dataset is sub-divided into 4 folds each containing 5 classes. A fold contains labelled samples from 5 classes that are used for evaluating the few-shot learning method. The rest 15 classes are used for training. Source: AMP: Adaptive Masked Proxies for Few-Shot Segmentation Image Source: https://arxiv.org/pdf/1709.03410.pdf

Procgen Benchmark includes 16 simple-to-use procedurally-generated environments which provide a direct measure of how quickly a reinforcement learning agent learns generalizable skills.

The PAMAP2 Physical Activity Monitoring dataset contains data of 18 different physical activities (such as walking, cycling, playing soccer, etc.), performed by 9 subjects wearing 3 inertial measurement units and a heart rate monitor. The dataset can be used for activity recognition and intensity estimation, while developing and applying algorithms of data processing, segmentation, feature extraction and classification. ** Sensors ** 3 Colibri wireless inertial measurement units (IMU): - sampling frequency: 100Hz - position of the sensors: - 1 IMU over the wrist on the dominant arm - 1 IMU on the chest - 1 IMU on the dominant side's ankle HR-monitor: - sampling frequency: ~9Hz ** Data collection protocol ** Each of the subjects had to follow a protocol, containing 12 different activities. The folder Protocol contains these recordings by subject. Furthermore, some of the subjects also performed a few optional activities. The folder Optional contains these recordings by subject. ** Data files ** Raw sensory data can be found in space-separated text-files (.dat), 1 data file per subject per session (protocol or optional). Missing values are indicated with NaN. One line in the data files correspond to one timestamped and labeled instance of sensory data. The data files contain 54 columns: each line consists of a timestamp, an activity label (the ground truth) and 52 attributes of raw sensory data. Source: UCI

The UCF-QNRF dataset is a crowd counting dataset and it contains large diversity both in scenes, as well as in background types. It consists of 1535 images high-resolution images from Flickr, Web Search and Hajj footage. The number of people (i.e., the count) varies from 50 to 12,000 across images. Source: Understanding the impact of mistakes on background regions in crowd counting Image Source: https://www.crcv.ucf.edu/data/ucf-qnrf/

The 3DMATCH benchmark evaluates how well descriptors (both 2D and 3D) can establish correspondences between RGB-D frames of different views. The dataset contains 2D RGB-D patches and 3D patches (local TDF voxel grid volumes) of wide-baselined correspondences. The pixel size of each 2D patch is determined by the projection of the 0.3m3 local 3D patch around the interest point onto the image plane. Source: 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions

The nocaps benchmark consists of 166,100 human-generated captions describing 15,100 images from the OpenImages validation and test sets. Source: nocaps: novel object captioning at scale Image Source: https://nocaps.org/

NExT-QA is a VideoQA benchmark targeting the explanation of video contents. It challenges QA models to reason about the causal and temporal actions and understand the rich object interactions in daily activities, e.g., "why is the boy crying?" and "How does the lady react after the boy fall backward?". It supports both multi-choice and generative open-ended QA tasks. The videos are untrimmed and the questions usually invoke local video contents for answers.

R2R is a dataset for visually-grounded natural language navigation in real buildings. The dataset requires autonomous agents to follow human-generated navigation instructions in previously unseen buildings, as illustrated in the demo above. For training, each instruction is associated with a Matterport3D Simulator trajectory. 22k instructions are available, with an average length of 29 words. There is a test evaluation server for this dataset available at EvalAI. Source: Natural language interaction with robots

SNAP is a collection of large network datasets. It includes graphs representing social networks, citation networks, web graphs, online communities, online reviews and more. Social networks : online social networks, edges represent interactions between people Networks with ground-truth communities : ground-truth network communities in social and information networks Communication networks : email communication networks with edges representing communication Citation networks : nodes represent papers, edges represent citations Collaboration networks : nodes represent scientists, edges represent collaborations (co-authoring a paper) Web graphs : nodes represent webpages and edges are hyperlinks Amazon networks : nodes represent products and edges link commonly co-purchased products Internet networks : nodes represent computers and edges communication Road networks : nodes represent intersections and edges roads connecting the intersections Autonomous systems : graphs of the internet Signed networks : networks with positive and negative edges (friend/foe, trust/distrust) Location-based online social networks : social networks with geographic check-ins Wikipedia networks, articles, and metadata : talk, editing, voting, and article data from Wikipedia Temporal networks : networks where edges have timestamps Twitter and Memetracker : memetracker phrases, links and 467 million Tweets Online communities : data from online communities such as Reddit and Flickr Online reviews : data from online review systems such as BeerAdvocate and Amazon User actions : actions of users on social platforms. Face-to-face communication networks : networks of face-to-face (non-online) interactions Graph classification datasets : disjoint graphs from different classes Image Source: https://snap.stanford.edu/data/

ALFRED (Action Learning From Realistic Environments and Directives), is a new benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. Source: ALFRED

The dataset consists of 400 whole-slide images (WSIs) of lymph node sections stained with hematoxylin and eosin (H&E), collected from two medical centers in the Netherlands. The WSIs are stored in a multi-resolution pyramid format, allowing for efficient retrieval of image subregions at different magnification levels. The training set includes two subsets: - 170 WSIs (100 normal, 70 with metastases) from Radboud University Medical Center - 100 WSIs (60 normal, 40 with metastases) from University Medical Center Utrecht The test set consists of 130 WSIs from both institutions. Ground truth data for metastases is provided as XML files with annotated contours and WSI binary masks. The Camelyon16 dataset aims to reduce the workload and subjectivity in cancer diagnosis by pathologists. It serves as a benchmark for evaluating algorithms that can automatically detect metastases in histopathological images, focusing on breast cancer in sentinel lymph nodes. Researchers can develop and refine machine learning models for automated detection of metastases. The dataset allows for performance comparisons of different detection algorithms. Automated systems can be integrated into clinical workflows to enhance diagnostic accuracy and efficiency. The dataset is valuable for training medical professionals in digital pathology and AI applications in diagnostics.

PAWS-X contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. All translated pairs are sourced from examples in PAWS-Wiki. Source: Google Research Image Source: https://arxiv.org/pdf/1908.11828v1.pdf

The Real-world Affective Faces Database (RAF-DB) is a dataset for facial expression. It contains 29672 facial images tagged with basic or compound expressions by 40 independent taggers. Images in this database are of great variability in subjects' age, gender and ethnicity, head poses, lighting conditions, occlusions, (e.g. glasses, facial hair or self-occlusion), post-processing operations (e.g. various filters and special effects), etc. Source: Landmark Guidance Independent Spatio-channel Attention and Complementary Context Information based Facial Expression Recognition Image Source: http://www.whdeng.cn/raf/model1.html

ATOMIC is an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge. Compared to existing resources that center around taxonomic knowledge, ATOMIC focuses on inferential knowledge organized as typed if-then relations with variables (e.g., "if X pays Y a compliment, then Y will likely return the compliment"). Source: ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning Image Source: https://homes.cs.washington.edu/~msap/atomic/

The UCY dataset consist of real pedestrian trajectories with rich multi-human interaction scenarios captured at 2.5 Hz (Δt=0.4s). It is composed of three sequences (Zara01, Zara02, and UCY), taken in public spaces from top-view. Source: Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data Image Source: http://trajnet.stanford.edu/data.php?n=1

LAION-400M is a dataset with CLIP-filtered 400 million image-text pairs, their CLIP embeddings and kNN indices that allow efficient similarity search. #### ⚠️ Disclaimer & Content Warning (from the authors) Our filtering protocol only removed NSFW images detected as illegal, but the dataset still has NSFW content accordingly marked in the metadata. When freely navigating through the dataset, keep in mind that it is a large-scale, non-curated set crawled from the internet for research purposes, such that collected links may lead to discomforting and disturbing content. Therefore, please use the demo links with caution. You can extract a “safe” subset by filtering out samples drawn with NSFW or via stricter CLIP filtering. There is a certain degree of duplication because we used URL+text as deduplication criteria. The same image with the same caption may sit at different URLs, causing duplicates. The same image with other captions is not, however, considered duplicated. Using KNN clustering should make it easy to further deduplicate by image content.

Fer2013 contains approximately 30,000 facial RGB images of different expressions with size restricted to 48×48, and the main labels of it can be divided into 7 types: 0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral. The Disgust expression has the minimal number of images – 600, while other labels have nearly 5,000 samples each. Source: Eavesdrop the Composition Proportion of Training Labels in Federated Learning Image Source: https://medium.com/@birdortyedi_23820/deep-learning-lab-episode-3-fer2013-c38f2e052280

SentEval is a toolkit for evaluating the quality of universal sentence representations. SentEval encompasses a variety of tasks, including binary and multi-class classification, natural language inference and sentence similarity. The set of tasks was selected based on what appears to be the community consensus regarding the appropriate evaluations for universal sentence representations. The toolkit comes with scripts to download and preprocess datasets, and an easy interface to evaluate sentence encoders. Source: SentEval: An Evaluation Toolkit for Universal Sentence Representations