2D Classification

Includes 950 real-world underwater images, 890 of which have the corresponding reference images. Source: An Underwater Image Enhancement Benchmark Dataset and Beyond

The VisA dataset contains 12 subsets corresponding to 12 different objects as shown in the above figure. There are 10,821 images with 9,621 normal and 1,200 anomalous samples. Four subsets are different types of printed circuit boards (PCB) with relatively complex structures containing transistors, capacitors, chips, etc. For the case of multiple instances in a view, we collect four subsets: Capsules, Candles, Macaroni1 and Macaroni2. Instances in Capsules and Macaroni2 largely differ in locations and poses. Moreover, we collect four subsets including Cashew, Chewing gum, Fryum and Pipe fryum, where objects are roughly aligned. The anomalous images contain various flaws, including surface defects such as scratches, dents, color spots or crack, and structural defects like misplacement or missing parts.

A large dataset of human hand images (dorsal and palmar sides) with detailed ground-truth information for gender recognition and biometric identification. Source: 11K Hands: Gender recognition and biometric identification using a large dataset of hand images

iSAID contains 655,451 object instances for 15 categories across 2,806 high-resolution images. The images of iSAID is the same as the DOTA-v1.0 dataset, which are manily collected from the Google Earth, some are taken by satellite JL-1, the others are taken by satellite GF-2 of the China Centre for Resources Satellite Data and Application. Source: iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images Image Source: iSAID

The Light Field Saliency Database (LFSD) contains 100 light fields with 360×360 spatial resolution. A rough focal stack and an all-focus image are provided for each light field. The images in this dataset usually have one salient foreground object and a background with good color contrast. Source: Light Field Saliency Detection with Deep Convolutional Networks Image Source: https://sites.duke.edu/nianyi/publication/saliency-detection-on-light-field/

MPIIGaze is a dataset for appearance-based gaze estimation in the wild. It contains 213,659 images collected from 15 participants during natural everyday laptop use over more than three months. It has a large variability in appearance and illumination. Source: https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild Image Source: https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild

OPV2V is a large-scale open simulated dataset for Vehicle-to-Vehicle perception. It contains over 70 interesting scenes, 11,464 frames, and 232,913 annotated 3D vehicle bounding boxes, collected from 8 towns in CARLA and a digital town of Culver City, Los Angeles.

ARKitScenes is an RGB-D dataset captured with the widely available Apple LiDAR scanner. Along with the per-frame raw data (Wide Camera RGB, Ultra Wide camera RGB, LiDar scanner depth, IMU) the authors also provide the estimated ARKit camera pose and ARKit scene reconstruction for each iPad Pro sequence. In addition to the raw and processed data from the mobile device, ARKit.

ApolloScape is a large dataset consisting of over 140,000 video frames (73 street scene videos) from various locations in China under varying weather conditions. Pixel-wise semantic annotation of the recorded data is provided in 2D, with point-wise semantic annotation in 3D for 28 classes. In addition, the dataset contains lane marking annotations in 2D. Source: A2D2: Audi Autonomous Driving Dataset Image Source: https://arxiv.org/pdf/1803.06184.pdf

VisDrone is a large-scale benchmark with carefully annotated ground-truth for various important computer vision tasks, to make vision meet drones. The VisDrone2019 dataset is collected by the AISKYEYE team at Lab of Machine Learning and Data Mining, Tianjin University, China. The benchmark dataset consists of 288 video clips formed by 261,908 frames and 10,209 static images, captured by various drone-mounted cameras, covering a wide range of aspects including location (taken from 14 different cities separated by thousands of kilometers in China), environment (urban and country), objects (pedestrian, vehicles, bicycles, etc.), and density (sparse and crowded scenes). Note that, the dataset was collected using various drone platforms (i.e., drones with different models), in different scenarios, and under various weather and lighting conditions. These frames are manually annotated with more than 2.6 million bounding boxes of targets of frequent interests, such as pedestrians, cars, bicycles, and tricycles. Some important attributes including scene visibility, object class and occlusion, are also provided for better data utilization. Source: https://github.com/VisDrone/VisDrone-Dataset Image Source: https://arxiv.org/pdf/2001.06303.pdf

SPair-71k contains 70,958 image pairs with diverse variations in viewpoint and scale. Compared to previous datasets, it is significantly larger in number and contains more accurate and richer annotations. Source: SPair-71k: A Large-scale Benchmark for Semantic Correspondence Image Source: http://cvlab.postech.ac.kr/research/SPair-71k/

Few-Shot Object Detection Dataset (FSOD) is a high-diverse dataset specifically designed for few-shot object detection and intrinsically designed to evaluate thegenerality of a model on novel categories. Source: FSOD

PASCAL-Part is a set of additional annotations for PASCAL VOC 2010. It goes beyond the original PASCAL object detection task by providing segmentation masks for each body part of the object. For categories that do not have a consistent set of parts (e.g., boat), it provides the silhouette annotation. It can also serve as a set for human semantic part segmentation: It contains multiple humans per image in unconstrained poses and occlusions (1,716 for training and 1,817 for testing). It provides careful pixel-wise annotations for six body parts (i.e., head, torso, upper/lower-arms, and upper-/lower-legs). Source: The Ultimate Theory of Human Parsing Image Source: https://www.researchgate.net/profile/Zhedong_Zheng/publication/328123707/figure/fig4/AS:704683136016384@1545020960225/Qualitative-parsing-results-on-the-Pascal-Person-Part-dataset.png

Wildtrack is a large-scale and high-resolution dataset. It has been captured with seven static cameras in a public open area, and unscripted dense groups of pedestrians standing and walking. Together with the camera frames, we provide an accurate joint (extrinsic and intrinsic) calibration, as well as 7 series of 400 annotated frames for detection at a rate of 2 frames per second. This results in over 40 000 bounding boxes delimiting every person present in the area of interest, for a total of more than 300 individuals.

ESD is an Emotional Speech Database for voice conversion research. The ESD database consists of 350 parallel utterances spoken by 10 native English and 10 native Chinese speakers and covers 5 emotion categories (neutral, happy, angry, sad and surprise). More than 29 hours of speech data were recorded in a controlled acoustic environment. The database is suitable for multi-speaker and cross-lingual emotional voice conversion studies.

Contains 51,583 descriptions of 11,046 objects from 800 ScanNet scenes. ScanRefer is the first large-scale effort to perform object localization via natural language expression directly in 3D. Source: ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language

PanNuke is a semi automatically generated nuclei instance segmentation and classification dataset with exhaustive nuclei labels across 19 different tissue types. The dataset consists of 481 visual fields, of which 312 are randomly sampled from more than 20K whole slide images at different magnifications, from multiple data sources. In total the dataset contains 205,343 labeled nuclei, each with an instance segmentation mask. Source: PanNuke Dataset Extension, Insights and Baselines Image Source: https://jgamper.github.io/PanNukeDataset/

The Salient Person dataset (SIP) contains 929 salient person samples with different poses and illumination conditions. Source: Accurate RGB-D Salient Object Detection via Collaborative Learning Image Source: https://arxiv.org/pdf/1907.06781.pdf

ETH is a dataset for pedestrian detection. The testing set contains 1,804 images in three video clips. The dataset is captured from a stereo rig mounted on car, with a resolution of 640 x 480 (bayered), and a framerate of 13--14 FPS. Source: Scale-aware Fast R-CNN for Pedestrian Detection Image Source: https://medium.com/@zhenqinghu/pedestrian-detection-on-eth-data-set-with-faster-r-cnn-19d0a906f1d3

The Exclusively Dark (ExDARK) dataset is a collection of 7,363 low-light images from very low-light environments to twilight (i.e 10 different conditions) with 12 object classes (similar to PASCAL VOC) annotated on both image class level and local object bounding boxes. Source: https://github.com/cs-chan/Exclusively-Dark-Image-Dataset

Fisheye cameras are commonly employed for obtaining a large field of view in surveillance, augmented reality and in particular automotive applications. In spite of its prevalence, there are few public datasets for detailed evaluation of computer vision algorithms on fisheye images. WoodScape is an extensive fisheye automotive dataset named after Robert Wood who invented the fisheye camera in 1906. WoodScape comprises of four surround view cameras and nine tasks including segmentation, depth estimation, 3D bounding box detection and soiling detection. Semantic annotation of 40 classes at the instance level is provided for over 10,000 images and annotation for other tasks are provided for over 100,000 images. Source: https://github.com/valeoai/WoodScape Image Source: https://github.com/valeoai/WoodScape

DAQUAR (DAtaset for QUestion Answering on Real-world images) is a dataset of human question answer pairs about images. Source: A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input Image Source: https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/vision-and-language/visual-turing-challenge/

PandaSet is a dataset produced by a complete, high-precision autonomous vehicle sensor kit with a no-cost commercial license. The dataset was collected using one 360x360 mechanical spinning LiDAR, one forward-facing, long-range LiDRAR, and 6 cameras. The datasets contains more than 100 scenes, each of which is 8 seconds long, and provides 28 types of labels for object classification and 37 types of annotations for semantic segmentation.

Consists of 100 challenging video sequences captured from real-world traffic scenes (over 140,000 frames with rich annotations, including occlusion, weather, vehicle category, truncation, and vehicle bounding boxes) for object detection, object tracking and MOT system. Source: UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking Image Source: UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking

Virtual KITTI 2 is an updated version of the well-known Virtual KITTI dataset which consists of 5 sequence clones from the KITTI tracking benchmark. In addition, the dataset provides different variants of these sequences such as modified weather conditions (e.g. fog, rain) or modified camera configurations (e.g. rotated by 15◦). For each sequence we provide multiple sets of images containing RGB, depth, class segmentation, instance segmentation, flow, and scene flow data. Camera parameters and poses as well as vehicle locations are available as well. In order to showcase some of the dataset’s capabilities, we ran multiple relevant experiments using state-of-the-art algorithms from the field of autonomous driving. The dataset is available for download at https://europe.naverlabs.com/Research/Computer-Vision/Proxy-Virtual-Worlds.

NJU2K is a large RGB-D dataset containing 1,985 image pairs. The stereo images were collected from the Internet and 3D movies, while photographs were taken by a Fuji W3 camera. Source: Bifurcated Backbone Strategy for RGB-D Salient Object Detection Image Source: Depth saliency based on anisotropic center-surround difference

DVQA is a synthetic question-answering dataset on images of bar-charts.

In Clipart1k, the target domain classes to be detected are the same as those in the source domain. All the images for a clipart domain were collected from one dataset (i.e., CMPlaces) and two image search engines (i.e., Openclipart2 and Pixabay3). Search queries used are 205 scene classes (e.g., pasture) used in CMPlaces to collect various objects and scenes with complex backgrounds.

The Objectron dataset is a collection of short, object-centric video clips, which are accompanied by AR session metadata that includes camera poses, sparse point-clouds and characterization of the planar surfaces in the surrounding environment. In each video, the camera moves around the object, capturing it from different angles. The data also contain manually annotated 3D bounding boxes for each object, which describe the object’s position, orientation, and dimensions. The dataset consists of 15K annotated video clips supplemented with over 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes. To ensure geo-diversity, the dataset is collected from 10 countries across five continents. Source: https://github.com/google-research-datasets/Objectron Image Source: https://github.com/google-research-datasets/Objectron

MedleyDB, is a dataset of annotated, royalty-free multitrack recordings. It was curated primarily to support research on melody extraction. For each song melody f₀ annotations are provided as well as instrument activations for evaluating automatic instrument recognition. The original dataset consists of 122 multitrack songs out of which 108 include melody annotations. The songs in MedleyDB were obtained from the following sources: * Independent Artists (30 songs) * NYU's Dolan Recording Studio (32 songs) * Weathervane Music (25 songs) * Music Delta (35 songs) MedleyDB contains songs of a variety of musical genres: Singer/Songwriter, Classical, Rock, World/Folk, Fusion, Jazz, Pop, Musical Theatre, Rap. For each song three types of audio content are given: a mix, stems, and raw audio. All types of audio files are .wav files with a sample rate of 44.1 kHz and a bit depth of 16. Source: https://medleydb.weebly.com/ Image Source: https://medleydb.weebly.com/ Audio Source: https://zenodo.org/record/1438309

Synscapes is a synthetic dataset for street scene parsing created using photorealistic rendering techniques, and show state-of-the-art results for training and validation as well as new types of analysis. Source: Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing Image Source: https://7dlabs.com/synscapes-overview

A novel dataset and benchmark, which features 1482 RGB-D scans of 478 environments across multiple time steps. Each scene includes several objects whose positions change over time, together with ground truth annotations of object instances and their respective 6DoF mappings among re-scans. Source: RIO: 3D Object Instance Re-Localization in Changing Indoor Environments

COCO-O(ut-of-distribution) contains 6 domains (sketch, cartoon, painting, weather, handmake, tattoo) of COCO objects which are hard to be detected by most existing detectors. The dataset has a total of 6,782 images and 26,624 labelled bounding boxes.

POP909 is a dataset which contains multiple versions of the piano arrangements of 909 popular songs created by professional musicians. The main body of the dataset contains the vocal melody, the lead instrument melody, and the piano accompaniment for each song in MIDI format, which are aligned to the original audio files. Furthermore, annotations are provided of tempo, beat, key, and chords, where the tempo curves are hand-labelled and others are done by MIR algorithms. Source: https://arxiv.org/pdf/2008.07142.pdf Image Source: https://arxiv.org/pdf/2008.07142.pdf

A2D (Actor-Action Dataset) is a dataset for simultaneously inferring actors and actions in videos. A2D has seven actor classes (adult, baby, ball, bird, car, cat, and dog) and eight action classes (climb, crawl, eat, fly, jump, roll, run, and walk) not including the no-action class, which we also consider. The A2D has 3,782 videos with at least 99 instances per valid actor-action tuple and videos are labeled with both pixel-level actors and actions for sampled frames. The A2D dataset serves as a large-scale testbed for various vision problems: video-level single- and multiple-label actor-action recognition, instance-level object segmentation/co-segmentation, as well as pixel-level actor-action semantic segmentation to name a few.

The EPIC-KITCHENS-55 dataset comprises a set of 432 egocentric videos recorded by 32 participants in their kitchens at 60fps with a head mounted camera. There is no guiding script for the participants who freely perform activities in kitchens related to cooking, food preparation or washing up among others. Each video is split into short action segments (mean duration is 3.7s) with specific start and end times and a verb and noun annotation describing the action (e.g. ‘open fridge‘). The verb classes are 125 and the noun classes 331. The dataset is divided into one train and two test splits. Source: Egocentric Hand Track and Object-based Human Action Recognition Image Source: https://epic-kitchens.github.io/2020-100