19,997 machine learning datasets
19,997 dataset results
LaRS is the largest and most diverse panoptic maritime obstacle detection dataset.
We present a large-scale dataset for 3D urban scene understanding. Compared to existing datasets, our dataset consists of 75 outdoor urban scenes with diverse backgrounds, encompassing over 15,000 images. These scenes offer 360◦ hemispherical views, capturing diverse foreground objects illuminated under various lighting conditions. Additionally, our dataset encompasses scenes that are not limited to forward-driving views, addressing the limitations of previous datasets such as limited overlap and coverage between camera views. The closest pre-existing dataset for generalizable evaluation is DTU [2] (80 scenes) which comprises mostly indoor objects and does not provide multiple foreground objects or background scenes.
BioCoder is a benchmark developed to evaluate existing pre-trained models in generating bioinformatics code. In relation to function-code generation, BioCoder covers potential package dependencies, class declarations, and global variables. It incorporates 1026 functions and 1243 methods in Python and Java from GitHub and 253 examples from the Rosalind Project.
The rise of deepfake images, especially of well-known personalities, poses a serious threat to the dissemination of authentic information. To tackle this, we present a thorough investigation into how deepfakes are produced and how they can be identified. The cornerstone of our research is a rich collection of artificial celebrity faces, titled DeepFakeFace (DFF). We crafted the DFF dataset using advanced diffusion models and have shared it with the community through online platforms. This data serves as a robust foundation to train and test algorithms designed to spot deepfakes. We carried out a thorough review of the DFF dataset and suggest two evaluation methods to gauge the strength and adaptability of deepfake recognition tools. The first method tests whether an algorithm trained on one type of fake images can recognize those produced by other methods. The second evaluates the algorithm's performance with imperfect images, like those that are blurry, of low quality, or compressed.
Video sequences from a glasshouse environment in Campus Kleinaltendorf(CKA), University of Bonn, captured by PATHoBot, a glasshouse monitoring robot.
SODA-D is a large-scale dataset tailored for small object detection in driving scenario, which is built on top of MVD dataset and owned data, where the former is a dataset dedicated to pixel-level understanding of street scenes, and the latter is mainly captured by onboard cameras and mobile phones. With 24704 well-chosen and high-quality images of driving scenarios, SODA-D comprises 277596 instances of 9 categories with horizontal bounding boxes.
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We leverage knowledge from foundation models to introduce Grasp-Anything, a new large-scale dataset with 1M (one million) samples and 3M objects, substantially surpassing prior datasets in diversity and magnitude. In addition, Grasp-Anything can universally cover objects in our daily lives and offer a great range of object diversity.
The dataset collected by the paper Seeing Dynamic Scene in the Dark: High-Quality Video Dataset with Mechatronic Alignment, ICCV 2021
VidChapters-7M is a dataset of 817K user-chaptered videos including 7M chapters in total. VidChapters-7M is automatically created from videos online in a scalable manner by scraping user-annotated chapters and hence without any additional manual annotation. It is designed for training and evaluating models for video chapter generation with or without ground-truth boundaries, and video chapter grounding, as well as for video-language pretraining.
MulRan is a dataset for Place Recognition and SLAM. The datasets were recorded in urban areas and contain sensor data of a car that is equipped with a 3D LiDAR (OS1-64) and a rotating radar (Navtech CIR204-H). For each sequence, the car is revisiting places several times.
The SOFC-Exp corpus contains 45 scientific publications about solid oxide fuel cells (SOFCs), published between 2013 and 2019 as open-access articles all with a CC-BY license. The dataset was manually annotated by domain experts.
GitHub repository for "AdaptiX – A Transitional XR Framework for Development and Evaluation of Shared Control Applications in Assistive Robotics", which is used in several shared control applications
CodRED is the first human-annotated cross-document relation extraction (RE) dataset, aiming to test the RE systems’ ability of knowledge acquisition in the wild. CodRED has the following features:
InsPLAD is a Dataset for Power Line Asset Inspection containing 10,607 high-resolution Unmanned Aerial Vehicles colour images. It contains 17 unique power line assets captured from real-world operating power lines. Some of those assets (five, to be precise) are also annotated regarding their conditions. They present the following defects: corrosion (4 of them), broken/missing cap (1 of them), and bird's nest presence (1 of them).
voraus-AD contains machine data of a collaborative robot, which moves a can by performing an industrial pick-and-place task. The samples consist of time series of machine data, each recorded over one pick-and-place operation. As usual in anomaly detection, the training set contains only normal data, which includes regular samples without anomalies. The test set contains both, normal data and anomalies, including 12 diverse anomaly types. In order to create a realistic scenario, we have divided the normal data into training and test data as follows: Up to a certain period of time, only training data including 948 samples was recorded. Subsequently, recordings of anomalies (755 samples) and normal data (419 samples) for the test set were taken alternately. This simulates a real application where training data would be recorded first in the same way to train the model before the test case occurs. To exclude temperature effects, we let robots warm up for half an hour before each recording.
A deductive reasoning benchmark based on formal logic theory. A model is required to generate a proof that (dis-) proves a given hypothesis based on a given set of facts.
The Song Describer Dataset (SDD) contains ~1.1k captions for 706 permissively licensed music recordings. It is designed for use in evaluation of models that address music-and-language (M&L) tasks such as music captioning, text-to-music generation and music-language retrieval.
The goal of this dataset is to probe video-language models for understanding of simple temporal relations like "before" and "after". The dataset is only meant to be an evaluation set and not a training set.
Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside the skull or brain, which can be attributed to various factors. Identifying, localizing and quantifying ICH has important clinical implications, in a bleed-dependent manner. While deep learning techniques are widely used in medical image segmentation and have been applied to the ICH segmentation task, existing public ICH datasets do not support the multi-class segmentation problem. To address this, we develop the Brain Hemorrhage Segmentation Dataset (BHSD), which provides a 3D multi-class ICH dataset containing 192 volumes with pixel-level annotations and 2200 volumes with slice-level annotations across five categories of ICH. To demonstrate the utility of the dataset, we formulate a series of supervised and semi-supervised ICH segmentation tasks. We provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset.