3,275 machine learning datasets
3,275 dataset results
To form the collection of nighttime RAW samples, we first selected a total of 150 images with the spatial resolution at 3464×5202 from the training and validation sets provided by the night image challenge. And then these RAW images are pre-processed to best produce noise-free samples using a notable CNN based denoiser. This is because nighttime imaging experiences a very challenging situation with heavy noises incurred by high ISO setting under poor illumination condition (e.g., underexposure).
Includes several sets of synthetic stereo images labelled with grasp rectangles representing parallel-jaw grasps (Cornell-like format).
Each HDF5 file has the following structure:
PLAD is a dataset where sparse depth is provided by line-based visual SLAM to verify StructMDC.
Danish Airs and Grounds (DAG) is a large collection of street-level and aerial images targeting such cases. Its main challenge lies in the extreme viewing-angle difference between query and reference images with consequent changes in illumination and perspective. The dataset is larger and more diverse than current publicly available data, including more than 50 km of road in urban, suburban and rural areas. All images are associated with accurate 6-DoF metadata that allows the benchmarking of visual localization methods.
A Sentinel-2 based time series multi country benchmark dataset, tailored for agricultural monitoring applications with Machine and Deep Learning. Sen4AgriNet dataset is annotated from farmer declarations collected via the Land Parcel Identification System (LPIS) for harmonizing country wide labels. Sen4AgriNet is the only multi-country, multi-year dataset that includes all spectral information. It is constructed to cover the period 2016-2020 for Catalonia and France, while it can be extended to include additional countries. Currently, it contains 42.5 million parcels, which makes it significantly larger than other available archives.
YouTubeGun Detection Dataset is collected from 343 high-definition YouTube videos and contains 5000 well-chosen images, in which 16064 instances of gun and 9046 instances of person are annotated. Compared to other datasets, YouTube-GDD is "dynamic", containing rich contextual information
ANUBIS is a large-scale human skeleton dataset containing 80 actions. Compared with previously collected datasets, ANUBIS is advantageous in the following four aspects: (1) employing more recently released sensors; (2) containing novel back view; (3) encouraging high enthusiasm of subjects; (4) including actions of the COVID pandemic era.
A large synthetic multi-camera crowd counting dataset with a large number of scenes and camera views to capture many possible variations, which avoids the difficulty of collecting and annotating such a large real dataset.
A visible-light and thermal-infrared images dataset for dual-spectrum depth estimation.
Charlotte-ThermalFace is a thermal face dataset. The data is fully annotated with the facial landmarks, ambient temperature, relative humidity, the air speed of the room, distance to the camera, and subject thermal sensation at the time of capturing each image.
Synthetic datasets have successfully been used to probe visual question-answering datasets for their reasoning abilities. CLEVR, for example, tests a range of visual reasoning abilities. The questions in CLEVR focus on comparisons of shapes, colors, and sizes, numerical reasoning, and existence claims. This paper introduces a minimally biased, diagnostic visual question-answering dataset, QLEVR, that goes beyond existential and numerical quantification and focus on more complex quantifiers and their combinations, e.g., asking whether there are more than two red balls that are smaller than at least three blue balls in an image. We describe how the dataset was created and present a first evaluation of state-of-the-art visual question-answering models, showing that QLEVR presents a formidable challenge to our current models.
Dynamic occupancy grids generated from NuScenes dataset. Dataset contains static environment and semantic labels, useful for long term prediction tasks.
This dataset is collected by DataCluster Labs, India. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai
Minecraft Corpus dataset with builder utterance annotations
The task addresses the problem of the appearance and propagation of posts that share misleading multimedia content (images or video). In the context of the task, different types of misleading use are considered:
Several datasets are fostering innovation in higher-level functions for everyone, everywhere. By providing this repository, we hope to encourage the research community to focus on hard problems. In this repository, we present our severity rates (BIRADS) of clinicians while diagnosing several patients from our User Tests and Analysis 4 (UTA4) study. Here, we provide a dataset for the measurements of severity rates (BIRADS) concerning the patient diagnostic. Work and results are published on a top Human-Computer Interaction (HCI) conference named AVI 2020 (page). Results were analyzed and interpreted from our Statistical Analysis charts. The user tests were made in clinical institutions, where clinicians diagnose several patients for a Single-Modality vs Multi-Modality comparison. For example, in these tests, we used both prototype-single-modality and prototype-multi-modality repositories for the comparison. On the same hand, the hereby dataset represents the pieces of information of bot
It contains grayscale mono and stereo images (NavCam and LocCam) from laboratory tests performed by a prototype rover on a martian-like testbed. The dataset can be used for artificial sample-tube detection and pose estimation. It also contains synthetic color images of the sample tube on a martian scenario created with Unreal Engine.
PolyU-BPCoMa: A Dataset and Benchmark Towards Mobile Colorized Mapping Using a Backpack Multisensorial System
The Reddit Photo Critique Dataset (RPCD) contains tuples of image and photo critiques. RPCD consists of 74K images and 220K comments and is collected from a Reddit community used by hobbyists and professional photographers to improve their photography skills by leveraging constructive community feedback.