1,019 machine learning datasets
1,019 dataset results
Largest, first-of-its-kind, in-the-wild, fine-grained workout/exercise posture analysis dataset, covering three different exercises: BackSquat, Barbell Row, and Overhead Press. Seven different types of exercise errors are covered. Unlabeled data is also provided to facilitate self-supervised learning.
The HOPE-Video dataset contains 10 video sequences (2038 frames) with 5-20 objects on a tabletop scene captured by a robot arm-mounted RealSense D415 RGBD camera. In each sequence, the camera is moved to capture multiple views of a set of objects in the robotic workspace. First COLMAP was applied to refine the camera poses (keyframes at 6~fps) provided by forward kinematics and RGB calibration from RealSense to Baxter's wrist camera. 3D dense point cloud was then generated via CascadeStereo (included for each sequence in 'scene.ply'). Ground truth poses for the HOPE objects models in the world coordinate system were annotated manually using the CascadeStereo point clouds. The following are provided for each frame:
The dataset has been designed to represent true web videos in the wild, with good visual quality and diverse content characteristics, The test video collection for TRECVID-AVS2019-TRECVID-AVS2021, which contains 1,082,649 web video clips, with even more diverse content, no predominant characteristics and low self-similarity.
OSAI introduces OpenTTGames - an open dataset aimed at evaluation of different computer vision tasks in Table Tennis: ball detection, semantic segmentation of humans, table and scoreboard and fast in-game events spotting.
Situated Dialogue Navigation (SDN) is a navigation benchmark of 183 trials with a total of 8415 utterances, around 18.7 hours of control streams, and 2.9 hours of trimmed audio. SDN is developed to evaluate the agent's ability to predict dialogue moves from humans as well as generate its own dialogue moves and physical navigation actions.
Caltech Fish Counting Dataset (CFC) is a large-scale dataset for detecting, tracking, and counting fish in sonar videos. This dataset contains over 1,500 videos sourced from seven different sonar cameras.
Tragic Talkers is an audio-visual dataset consisting of excerpts from the "Romeo and Juliet" drama captured with microphone arrays and multiple co-located cameras for light-field video. Tragic Talkers provides ideal content for object-based media (OBM) production. It is designed to cover various conventional talking scenarios, such as monologues, two-people conversations, and interactions with considerable movement and occlusion, yielding 30 sequences captured from a total of 22 different points of view and two 16-element microphone arrays.
The TbV dataset is large-scale dataset created to allow the community to improve the state of the art in machine learning tasks related to mapping, that are vital for self-driving.
SurgT is a dataset for benchmarking 2D Trackers in Minimally Invasive Surgery (MIS). It contains a total of 157 stereo endoscopic videos from 20 clinical cases, along with stereo camera calibration parameters.
DIVOTrack is a cross-view multi-object tracking dataset for DIVerse Open scenes with dense tracking pedestrians in realistic and non-experimental environments. DIVOTrack has ten distinct scenarios and 550 cross-view tracks.
BUAA-MIHR dataset is a remote photoplethysmography (rPPG) dataset. BUAA-MIHR dataset for evaluation of remote photoplethysmography pipeline under multi-illumination situations. We recruited 15 healthy subjects (12 male, 3 female, 18 to 30 years old) in this experiment and a total number of 165 video sequences were recorded under various illuminations. The experiments were conducted in a darkroom in order to isolate from ambient light.
YTD-18M is a large-scale corpus of 18M video-based dialogues, constructed from web videos: crucial to the data collection pipeline is a pretrained language model that converts error-prone automatic transcripts to a cleaner dialogue format while maintaining meaning.
From Grounded Human-Object Interaction Hotspots from Video (ICCV'19): We collect annotations for interaction keypoints on EPIC Kitchens in order to quantitatively evaluate our method in parallel to the OPRA dataset (where annotations are available). We note that these annotations are collected purely for evaluation, and are not used for training our model. We select the 20 most frequent verbs, and select 31 nouns that afford these interactions.
RoboBEV is a robustness evaluation benchmark tailored for camera-based bird's eye view (BEV) perception under natural data corruptions and domain shift. It includes eight distinct corruptions, including Bright, Dark, Fog, Snow, Motion Blur, Color Quant, Camera Crash, and Frame Lost.
FunQA is a challenging video question answering (QA) dataset specifically designed to evaluate and enhance the depth of video reasoning based on counter-intuitive and fun videos. Unlike most video QA benchmarks which focus on less surprising contexts, e.g., cooking or instructional videos, FunQA covers three previously unexplored types of surprising videos: 1) HumorQA, 2) CreativeQA, and 3) MagicQA. For each subset, we establish rigorous QA tasks designed to assess the model's capability in counter-intuitive timestamp localization, detailed video description, and reasoning around counter-intuitiveness. In total, the FunQA benchmark consists of 312K free-text QA pairs derived from 4.3K video clips, spanning a total of 24 video hours. Extensive experiments with existing VideoQA models reveal significant performance gaps for the FunQA videos across spatial-temporal reasoning, visual-centered reasoning, and free-text generation.
The dataset is designed specifically to solve a range of computer vision problems (2D-3D tracking, posture) faced by biologists while designing behavior studies with animals.
Polyps in the colon are widely known cancer precursors identified by colonoscopy. Whilst most polyps are benign, the polyp’s number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason being the lack of a comprehensive public dataset. As a result, the developed methods may not generalise to different population datasets. To this extent, we have curated a dataset from six unique centres incorporating more than 300 patients. The dataset includes both single frame and sequence data with 3762 annotated polyp labels with precise delineation of polyp boundaries verified by six senior gastroenterologists. To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as PolypGen) curated by a team of computational scientists and exper
OpenLane-V2 is the world's first perception and reasoning benchmark for scene structure in autonomous driving. The primary task of the dataset is scene structure perception and reasoning, which requires the model to recognize the dynamic drivable states of lanes in the surrounding environment. The challenge of this dataset includes not only detecting lane centerlines and traffic elements but also recognizing the attribute of traffic elements and topology relationships on detected objects.
The MISP2021 challenge dataset is a collection of audio-visual conversational data recorded in a home TV scenario using distant multi-microphones. The dataset captures interactions between several individuals who are engaged in conversations in Chinese while watching TV and interacting with a smart speaker/TV in a living room. The dataset is extensive, comprising 141 hours of audio and video data, which were collected using far/middle/near microphones and far/middle cameras in 34 real-home TV rooms. Notably, this corpus is the first of its kind to offer a distant multimicrophone conversational Chinese audio-visual dataset. Furthermore, it is also the first large vocabulary continuous Chinese lip-reading dataset specifically designed for the adverse home-TV scenario.
The WebVid-CoVR dataset is a collection of video-text-video triplets that can be used for the task of composed video retrieval (CoVR). CoVR is a task that involves searching for videos that match both a query image and a query text. The text typically specifies the desired modification to the query image.