383 machine learning datasets
383 dataset results
Housekeep a benchmark to evaluate common sense reasoning in the home for embodied AI. In Housekeep, an embodied agent must tidy a house by rearranging misplaced objects without explicit instructions specifying which objects need to be rearranged. The dataset contains where humans typically place objects in tidy and untidy houses constituting 1799 objects, 268 object categories, 585 placements, and 105 rooms.
The Argoverse 2 Sensor Dataset is a collection of 1,000 scenarios with 3D object tracking annotations. Each sequence in our training and validation sets includes annotations for all objects within five meters of the “drivable area” — the area in which it is possible for a vehicle to drive. The HD map for each scenario specifies the driveable area.
Unite The People is a dataset for 3D body estimation. The images come from the Leeds Sports Pose dataset and its extended version, as well as the single person tagged people from the MPII Human Pose Dataset. The images are labeled with different types of annotations such as segmentation labels, pose or 3D.
xR-EgoPose is an egocentric synthetic dataset for egocentric 3D human pose estimation. It consists of ~380 thousand photo-realistic egocentric camera images in a variety of indoor and outdoor spaces.
The MM-WHS 2017 dataset is a dataset for multi-modality whole heart segmentation. It provides 20 labeled and 40 unlabeled CT volumes, as well as 20 labeled and 40 unlabeled MR volumes. In total there are 120 multi-modality cardiac images acquired in a real clinical environment.
AKB-48 is a large-scale Articulated object Knowledge Base which consists of 2,037 real-world 3D articulated object models of 48 categories.
Memory Maze is a 3D domain of randomized mazes designed for evaluating the long-term memory abilities of RL agents. Memory Maze isolates long-term memory from confounding challenges, such as exploration, and requires remembering several pieces of information: the positions of objects, the wall layout, and keeping track of agent’s own position.
Mindboggle is a large publicly available dataset of manually labeled brain MRI. It consists of 101 subjects collected from different sites, with cortical meshes varying from 102K to 185K vertices. Each brain surface contains 25 or 31 manually labeled parcels.
BLVD is a large scale 5D semantics dataset collected by the Visual Cognitive Computing and Intelligent Vehicles Lab. This dataset contains 654 high-resolution video clips owing 120k frames extracted from Changshu, Jiangsu Province, China, where the Intelligent Vehicle Proving Center of China (IVPCC) is located. The frame rate is 10fps/sec for RGB data and 3D point cloud. The dataset contains fully annotated frames which yield 249,129 3D annotations, 4,902 independent individuals for tracking with the length of overall 214,922 points, 6,004 valid fragments for 5D interactive event recognition, and 4,900 individuals for 5D intention prediction. These tasks are contained in four kinds of scenarios depending on the object density (low and high) and light conditions (daytime and nighttime).
We provide manual annotations of 14 semantic keypoints for 100,000 car instances (sedan, suv, bus, and truck) from 53,000 images captured from 18 moving cameras at Multiple intersections in Pittsburgh, PA. Please fill the google form to get a email with the download links:
The Zenseact Open Dataset (ZOD) is a large-scale and diverse multi-modal autonomous driving (AD) dataset, created by researchers at Zenseact. It was collected over a 2-year period in 14 different European counties, using a fleet of vehicles equipped with a full sensor suite. The dataset consists of three subsets: Frames, Sequences, and Drives, designed to encompass both data diversity and support for spatiotemporal learning, sensor fusion, localization, and mapping.
3DFAW contains 23k images with 66 3D face keypoint annotations.
The ETH SfM (structure-from-motion) dataset is a dataset for 3D Reconstruction. The benchmark investigates how different methods perform in terms of building a 3D model from a set of available 2D images.
Thingi10K is a dataset of 3D-Printing Models. Specifically there are 10,000 models from featured “things” on thingiverse.com, suitable for testing 3D printing techniques such as structural analysis , shape optimization, or solid geometry operations.
Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather.
Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires significant neuroanatomical expertise. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N=1271), a larger dataset of T1w stroke MRIs and manually segmented lesion masks that includes training (public. n=655), test (masks hidden, n=300), and generalizability (completely hidden, n=316) data. Algorithm development using this larger sample should lead to more robust solutions, and the hidden test and generalizability datasets allow for unbiased performance evaluation
X-Humans consists of 20 subjects (11 males, 9 females) with various clothing types and hair style. The collection of this dataset has been approved by an internal ethics committee. For each subject, we split the motion sequences into a training and test set. In total, there are 29,036 poses for training and 6,439 test poses. X-Humans also contains ground-truth SMPL-X parameters, obtained via a custom SMPL-X registration pipeline specifically designed to deal with low-resolution body parts.
T$^3$Bench is the first comprehensive text-to-3D benchmark containing diverse text prompts of three increasing complexity levels that are specially designed for 3D generation (300 prompts in total). To assess both the subjective quality and the text alignment, we propose two automatic metrics based on multi-view images produced by the 3D contents. The quality metric combines multi-view text-image scores and regional convolution to detect quality and view inconsistency. The alignment metric uses multi-view captioning and Large Language Model (LLM) evaluation to measure text-3D consistency.
We present the HANDAL dataset for category-level object pose estimation and affordance prediction. Unlike previous datasets, ours is focused on robotics-ready manipulable objects that are of the proper size and shape for functional grasping by robot manipulators, such as pliers, utensils, and screwdrivers. Our annotation process is streamlined, requiring only a single off-the-shelf camera and semi-automated processing, allowing us to produce high-quality 3D annotations without crowd-sourcing. The dataset consists of 308k annotated image frames from 2.2k videos of 212 real-world objects in 17 categories. We focus on hardware and kitchen tool objects to facilitate research in practical scenarios in which a robot manipulator needs to interact with the environment beyond simple pushing or indiscriminate grasping. We outline the usefulness of our dataset for 6-DoF category-level pose+scale estimation and related tasks. We also provide 3D reconstructed meshes of all objects, and we outline s
Autonomous trucking is a promising technology that can greatly impact modern logistics and the environment. Ensuring its safety on public roads is one of the main duties that requires an accurate perception of the environment. To achieve this, machine learning methods rely on large datasets, but to this day, no such datasets are available for autonomous trucks. In this work, we present MAN TruckScenes, the first multimodal dataset for autonomous trucking. MAN TruckScenes allows the research community to come into contact with truck-specific challenges, such as trailer occlusions, novel sensor perspectives, and terminal environments for the first time. It comprises more than 740 scenes of 20s each within a multitude of different environmental conditions. The sensor set includes 4 cameras, 6 lidar, 6 radar sensors, 2 IMUs, and a high-precision GNSS. The dataset's 3D bounding boxes were manually annotated and carefully reviewed to achieve a high quality standard. Bounding boxes are availa