19,997 machine learning datasets
19,997 dataset results
This dataset supports the research detailed in the pre-print "Virtual Imaging Trials Improved the Transparency and Reliability of AI Systems in COVID-19 Imaging." The study employs both clinical and simulated CT data to evaluate AI models for COVID-19 diagnosis. By leveraging the Virtual Imaging Trials (VIT) framework, the research addresses reproducibility and generalizability issues prevalent in medical imaging AI models.
A fully synthetic dataset of drones generated using structured domain randomization. It contains multiple datasets generated using different styles: - Drones only - Drones and Birds - Generic Distractors - Realistic Distractors - Random Backgrounds
COMFORT is an evaluation protocol to systematically assess the spatial reasoning capabilities of VLMs.
Abstract: Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous grap
Dataset Card for BeyondX
The dataset SFU-HW-Objects-v1 contains bounding boxes and object class labels for High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) video sequences. The presented dataset contains only object labels; raw video sequences themselves can be obtained from the Joint Collaborative Team on Video Coding (JCT-VC). The dataset is used in the MPEG-VCM (Video Coding for Machines) and MPEG-FCM (Feature Coding for Machines) standardization efforts.
This repository contains data for the NeurIPS conference paper titled "Harnessing Machine Learning for Single-Shot Measurement of Free Electron Laser Pulse Power".
Classifying Email as Spam or Non-Spam.
Due to the free-form nature of the open vocabulary image classification task, special annotations are required for image sets used for evaluation purposes. Three such image datasets are presented here:
This corpus contains data files that were generated as part of the NOVIC paper (see above). This includes the complete Object Noun Dictionary, the exact templates used for the multiset prompt templating strategy, and a large dataset of 1.8M LLM-generated and templated captions assorted by target noun. The captions were generated based on all of the target nouns in the Object Noun Dictionary.
DAVIS-Edit is a curated testing benchmark for video editing. This dataset contains two evaluation settings, i.e., text- and image-based editing. Besides, it offers two types of annotated for both modalities of prompts, considering the editing scenarios with similar (DAVIS-Edit-S) and changing (DAVIS-Edit-C) shapes, so as to address the shape inconsistency problem in video-to-video editing.
This dataset contains synthetic images extracted from the CARLA simulator along with rich information extracted from the deferred rendering pipeline of Unreal Engine 4. The main purpose of this dataset is the training of the state-of-the-art image-to-image translation model proposed by Intel Labs "Enhancing Photorealism Enhancement" (EPE). Translation results derived from the model targeting the characteristics of Cityscapes, KITTI, and Mapillary Vistas are also provided. Computer vision-based models trained on these data are expected to perform better when deployed in the real world.
The TIC Dataset consists of 2056 images (512x640) of transmission line network footage in Greece (Northeast Attica) and annotations of three object classes, i.e. towers, insulators and conductors. The dataset file contains the following: i. train dataset folder ("train"):
The dataset comprises of images of 10 types of fruits in both raw and ripe states.
Trip duration is the most fundamental measure in all modes of transportation. Hence, it is crucial to predict the trip-time precisely for the advancement of Intelligent Transport Systems (ITS) and traveller information systems. In order to predict the trip duration, data mining techniques are employed in this paper to predict the trip duration of rental bikes in Seoul Bike sharing system. The prediction is carried out with the combination of Seoul Bike data and weather data. The Data used include trip duration, trip distance, pickup-dropoff latitude and longitude, temperature, precipitation, wind speed, humidity, solar radiation, snowfall, ground temperature and 1-hour average dust concentration. Feature engineering is done to extract additional features from the data. Four statistical models are used to predict the trip duration. (a) Linear regression, (b) Gradient boosting machines, (c) k nearest neighbor and (d) Random Forest(RF). Four performance metrics Root mean squared error, Co
Energy consumption data is collected using IoT based systems and used for prediction. Data used include the lagging and leading current reactive power, the lagging and leading current power factor, carbon dioxide emissions, and load types.
This dataset is crated via finding the citation links between papers in CORD19 Dataset. We used only a small subset of the all graph by choosing papers that have 20 or more citation.
Overview: This collection contains three synthetic datasets produced by gpt-4o-mini for sentiment analysis and PDT (Product Desirability Toolkit) testing. Each dataset contains 1000 hypothetical software product reviews with the aim to produce a diversity of sentiment and text. The datasets were created as part of the research described in:
Large language models (LLMs) excel in high-resource languages but face notable challenges in low-resource languages like Mongolian. The release of MM-Eval, comprising 569 syntax, 677 semantics, 344 knowledge, and 250 reasoning tasks, offers valuable insights for advancing NLP and LLMs in low-resource languages like Mongolian.
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