19,997 machine learning datasets
19,997 dataset results
Description:
LigBoundConf is a set of high-quality drug-like bound ligand structures in the Protein Data Bank (PDB). The original set contains three sets of structures: "raw” structures taken directly from the PDB, "bound” structures, obtained by performing a local minimization of the ligand in the presence of the protein pocket with an empirical force field, and “global minimum” structures, obtained by finding the lowest energy conformation of the ligand with an empirical force field and implicit water solvation model.
The dataset consists of images of bananas and apples. It was created by collecting images, under the Creative Commons license, from Flickr. The images illustrate bananas and apples with variations regarding the color, placement, size, and background. The motivation for the construction of this dataset stems from studies in cognitive science, where human perception is investigated using examples with discrete properties of bananas and apples. It can be used in the context of explainable/interpretable image classification as in: Dimas, G., Cholopoulou, E., & Iakovidis, D. K. (2023). E pluribus unum interpretable convolutional neural networks. Scientific Reports, 13(1), 11421. https://www.nature.com/articles/s41598-023-38459-1
VETRA is a dataset for vehicle tracking in aerial image sequences and presents unique challenges such as low frame rates, small and fast-moving objects, as well as high camera movement. These characteristics allow for extended tracking of numerous vehicles with varying motion behaviors over large areas and pose new challenges for MOT algorithms. VETRA consists of 52 image sequences captured by airplanes and helicopters using DLR’s 3k and 4k camera systems. The acquisition sites are located in Germany and Austria. In addition to the classical training, validation and test sets, VETRA offers a second test set specifically designed for the application of large area monitoring (LAM). The LAM sequences are recorded over 7 rural roads and motorways with a fixed camera speed and configuration. Each road section is captured at 4 different times of the day, enabling the performance of MOT algorithms to be evaluated under different traffic loads in a static environment. Furthermore, the feature
We complement ARKitScenes dataset with dense semantic annotations that are automatically generated at scale. This produces the first large-scale, real-world 3D dataset with dense semantic annotations. Training on this auto-generated data, we push forward the state-of-the-art performance on ScanNet and ScanNet200 with prevalent 3D semantic segmentation models.
This dataset was developed within an analysis of research data generated and managed within the University of Bologna, with respect to the differences and commonalities between disciplines and potential challenges for institutional data support services and infrastructures. We are primarily mapping the type (e.g., image), content (e.g., scan of a manuscript) and format (e.g., .tiff) of managed data, thus sustaining the value of FAIR data as granular resources.
A benchmark extending needle-in-a-haystack (NIAH) test with a carefully designed needle set, where questions and needles have minimal lexical overlap, requiring models to infer latent associations to locate the needle within the haystack.
EnvBench is a comprehensive benchmark for automating environment setup - an important task in software engineering. We have collected the largest dataset to date for this task and introduced a robust framework for developing and evaluating LLM-based agents that tackle environment setup challenges.
The structure for the dataset is as follows :
MeshFleet is a filtered and annotated dataset of High Quality vehicles derived from Objaverse XL. It contains the sha256 of the objects together with consitent object captions and vehicle parameters.
The Songdo Traffic dataset delivers precisely georeferenced vehicle trajectories captured through high-altitude bird's-eye view (BeV) drone footage over Songdo International Business District, South Korea. Comprising approximately 700,000 unique trajectories, this resource represents one of the most extensive aerial traffic datasets publicly available, distinguishing itself through exceptional temporal resolution that captures vehicle movements at 29.97 points per second, enabling unprecedented granularity for advanced urban mobility analysis.
The Songdo Vision dataset provides high-resolution (4K, 3840×2160 pixels) RGB images annotated with categorized axis-aligned bounding boxes (BBs) for vehicle detection from a high-altitude bird’s-eye view (BeV) perspective. Captured over Songdo International Business District, South Korea, this dataset consists of 5,419 annotated video frames, featuring approximately 300,000 vehicle instances categorized into four classes:
ELTEX-Blockchain: A Domain-Specific Dataset for Cybersecurity 🔐 12k Synthetic Social Media Messages for Early Cyberattack Detection on Blockchain
https://arxiv.org/abs/2503.15222
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Data for a Kaggle competition
The BIRDeep Audio Annotations dataset is a collection of bird vocalizations from Doñana National Park, Spain. It was created as part of the BIRDeep project, which aims to optimize the detection and classification of bird species in audio recordings using deep learning techniques. The dataset is intended for use in training and evaluating models for bird vocalization detection and identification.
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The NCSE v2.0 is a digitized collection of six 19th-century English periodicals
A publicly available corpus of nineteenth-century newspaper text focused on crime in London, derived from the Gale British Library Newspapers corpus parts 1 and 2. The corpus comprises 600 newspaper excerpts and for each excerpt contains the original source image, the machine transcription of that image as found in the BLN and a gold standard manual transcription.