19,997 machine learning datasets
19,997 dataset results
The Foggy KITTI dataset extends the KITTI dataset to include challenging weather conditions, aiming to support research in real-world applications such as autonomous driving. It contains synthetic fog images with different levels of intensity and is divided into training and testing sets, providing a useful resource for developing and evaluating models in practical scenarios.
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Item-wise accuracies in six benchmarks from Open LLM Leaderboard 1 scraped from huggingface.co and used for metabench analyses and construction. Datasets with RMSE's for random benchmark subsets are used as reference in the paper and are included here.
Evaluate LLM on Real-world coding tasks.
We present ESG-FTSE, the first corpus comprised of news articles with Environmental, Social and Governance (ESG) relevance annotations. In recent years, investors and regulators have pushed ESG investing to the mainstream due to the urgency of climate change. This has led to the rise of ESG scores to evaluate an investment's credentials as socially responsible. While demand for ESG scores is high, their quality varies wildly. Quantitative techniques can be applied to improve ESG scores, thus, responsible investing. To contribute to resource building for ESG and financial text mining, we pioneer the ESG-FTSE corpus. We further present the first of its kind ESG annotation schema. It has three levels: a binary classification (relevant versus irrelevant news articles), ESG classification (ESG-related news articles), and target company. Both supervised and unsupervised learning experiments for ESG relevance detection were conducted to demonstrate that the corpus can be used in different set
Collecting data with a HIKVISION USB Camera DS-E11, we build a dataset called MentalHAD with four abnormal actions (climbing walls, hitting windows, climbing, and hitting) and six normal actions (crouching, standing, sitting, hand waving, walking, and running). It includes RGB videos of about 274 minutes (493504 frames) with 30 FPS in three different scenes, five subjects, and seven scene-subject pairs. They are organized into 69 sequences, each containing the data of only one action in about 2-5 minutes.
Overview The Spike-X4K Dataset is a high-resolution image reconstruction resource tailored for the latest advancements in spike camera technology. It is designed to meet the demands of modern spike cameras with a resolution of 1000×1000 pixels, surpassing the capabilities of previous datasets like spike-REDS, which was limited to a resolution of 250×400 pixels.
AsEP is a protein structure dataset that includes 1723 filtered antibody-antigen complexes from abYbank/AbDb.
Short-Films 20K (SF20K) is the largest publicly available movie dataset. SF20K is composed of 20,143 amateur films and offers long-term video tasks in the form of multiple-choice and open-ended question answering.
A large-scale isolated Indian sign language dataset. It contains 2002 common words, used in daily communications among Indian deaf community. The dataset contains 40033 videos across 2002 words. The total duration of the dataset is around 36.2 hours with 7.8 Million frames.
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We introduce GLObal Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for major cities worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse resolution urban canopy elevation data with a random forest model to estimate building-level information. Validation using LiDAR data from six U.S. cities showed UT-GLOBUS-derived building heights had an RMSE of 9.1 meters, and mean building height within 1-km² grid cells had an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (~55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset's utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, T
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