데이터셋 탐색

The See-in-the-Dark (SID) dataset contains 5094 raw short-exposure images, each with a corresponding long-exposure reference image. Images were captured using two cameras: Sony α7SII and Fujifilm X-T2.

Outside Knowledge Visual Question Answering (OK-VQA) includes more than 14,000 questions that require external knowledge to answer. Source: OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Image Source: https://okvqa.allenai.org/

SIDD is an image denoising dataset containing 30,000 noisy images from 10 scenes under different lighting conditions using five representative smartphone cameras. Ground truth images are provided along with the noisy images. Source: A High-Quality Denoising Dataset for Smartphone Cameras

A new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. Source: RESIDE

SIDER contains information on marketed medicines and their recorded adverse drug reactions. The information is extracted from public documents and package inserts. The available information include side effect frequency, drug and side effect classifications as well as links to further information, for example drug–target relations. Source: Side Effect Resource Image Source: http://sideeffects.embl.de/drugs/2756/

xSID, a new evaluation benchmark for cross-lingual (X) Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect, covering Arabic (ar), Chinese (zh), Danish (da), Dutch (nl), English (en), German (de), Indonesian (id), Italian (it), Japanese (ja), Kazakh (kk), Serbian (sr), Turkish (tr) and an Austro-Bavarian German dialect, South Tyrolean (de-st).

The first real-world, large-scale Roadside Cooperative Perception Dataset, RCooper, is released to bloom research on roadside cooperative perception for practical applications. More than 50k images and 30k point clouds manually annotated with 3D bounding boxes and trajectories for ten semantic classes are provided.

The Messidor database has been established to facilitate studies on computer-assisted diagnoses of diabetic retinopathy. The research community is welcome to test its algorithms on this database. In this section, you will find instructions on how to download the database. Source: MESSIDOR

The current OOD benchmark VQA-CP v2 only considers one type of shortcut (from question type to answer) and thus still cannot guarantee that the modelrelies on the intended solution rather than a solution specific to this shortcut. To overcome this limitation, VQA-VS proposes a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. In addition, VQA-VS overcomes three troubling practices in the use of VQA-CP v2, e.g., selecting models using OOD test sets, and further standardize OOD evaluation procedure. VQA-VS provides a more rigorous and comprehensive testbed for shortcut learning in VQA.

This dataset contains 1304 de-identified longitudinal medical records describing 296 patients.

This is the first image-based network intrusion detection dataset. This large-scale dataset included network traffic protocol communication-based images from 15 different observation locations of different countries in Asia. This dataset is used to identify two different types of anomalies from benign network traffic. Each image with a size of 48 × 48 contains multi-protocol communications within 128 seconds. The SIDD dataset can be to applied to a broad range of tasks such as machine learning-based network intrusion detection, non-iid federated learning, and so forth.

Original SID dataset is introduced in "Learning to See in the Dark". The subset of SID dataset captured by Sony α7S II camera is adopted for evaluation. There are 2697 short-long-exposure RAW image pairs. To make this dataset more challenging, we converted the RAW format images to sRGB images with no gamma correction, which resulted in images becoming extremely dark.

The Double-Sided Braille Image dataset (DSBI) is a large-scale dataset for Braille image recognition. It has detailed Braille recto dots, verso dots and Braille cells annotation. Source: https://arxiv.org/abs/1811.10893 Image Source: https://github.com/yeluo1994/DSBI

The Insider Threat Test Dataset is a collection of synthetic insider threat test datasets that provide both background and malicious actor synthetic data. The CERT Division, in partnership with ExactData, LLC, and under sponsorship from DARPA I2O, generated a collection of synthetic insider threat test datasets. These datasets provide both synthetic background data and data from synthetic malicious actors. For more background on this data, please see the paper, Bridging the Gap: A Pragmatic Approach to Generating Insider Threat Data. Datasets are organized according to the data generator release that created them. Most releases include multiple datasets (e.g., r3.1 and r3.2). Generally, later releases include a superset of the data generation functionality of earlier releases. Each dataset file contains a readme file that provides detailed notes about the features of that release. The answer key file answers.tar.bz2 contains the details of the malicious activity included in each dataset, including descriptions of the scenarios enacted and the identifiers of the synthetic users involved.

This database offers iris images (with and without contact lenses) of the same eyes captured shortly one after another with illumination coming from two different locations. 5,796 iris images in total were acquired by the LG IrisAccess 4000 sensor from 119 subjects. This set is divided into four subsets used in the experiments: (a) 1,800 images of irises wearing regular (with dot-like pattern) textured contact lenses, as shown in Fig. 6a in the wAcv 2019 paper; (b) 864 images of irises wearing irregular (without dot-like pattern) textured contact lenses, as shown in Fig. 6b in the WACV 2019 paper; (c) 1,728 images of irises wearing clear contact lenses (without any visible pattern), and (d) 1,404 images of authentic irises without any contact.

SIDAR is a dataset designed to be a training and evaluation set for a multitude of tasks involving image alignment and artifact removal, such as deep homography estimation, dense image matching, 2D bundle adjustment, inpainting, shadow removal, denoising, content retrieval, and background subtraction. Source: SIDAR: Synthetic Image Dataset for Alignment & Restoration

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The Caltech Resident-Intruder Mouse dataset (CRIM13) consists of 237x2 videos (recorded with synchronized top and side view) of pairs of mice engaging in social behavior, catalogued into thirteen different actions. Each video lasts ~10min, for a total of 88 hours of video and 8 million frames. A team of behavior experts annotated each video on a frame-by-frame basis for a state-of-the-art study of the neurophysiological mechanisms involved in aggression and courtship in mice. Source: https://pdollar.github.io/research.html Image Source: https://authors.library.caltech.edu/104600/1/2020.07.26.222299v1.full.pdf

Description - Repository: Code, Page, Data - Paper: arxiv.org/abs/2411.17440 - Point of Contact: Shenghai Yuan ## Citation If you find our paper and code useful in your research, please consider giving a star and citation. BibTeX @article{yuan2024identity, title={Identity-Preserving Text-to-Video Generation by Frequency Decomposition}, author={Yuan, Shenghai and Huang, Jinfa and He, Xianyi and Ge, Yunyuan and Shi, Yujun and Chen, Liuhan and Luo, Jiebo and Yuan, Li}, journal={arXiv preprint arXiv:2411.17440}, year={2024} }

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Smartphone cameras are ubiquitous in daily life, yet their performance can be severely impacted by dirty lenses, leading to degraded image quality. This issue is often overlooked in image restoration research, which assumes ideal or controlled lens conditions. To address this gap, we introduced SIDL (Smartphone Images with Dirty Lenses), a novel dataset designed to restore images captured through contaminated smartphone lenses. SIDL contains diverse real-world images taken under various lighting conditions and environments. These images feature a wide range of lens contaminants, including water drops, fingerprints, and dust. Each contaminated image is paired with a clean reference image, enabling supervised learning approaches for restoration tasks. To evaluate the challenge posed by SIDL, various state-of-the-art restoration models were trained and compared on this dataset. Their performances achieved some level of restoration but did not adequately address the diverse and realistic nature of the lens contaminants in SIDL. This challenge highlights the need for more robust and adaptable image restoration techniques for restoring images with dirty lenses. Project website: https://sidl-benchmark.github.io Source : SIDL: A Real-World Dataset for Restoring Smartphone Images with Dirty Lenses

SIDOD is a new, publicly-available image dataset generated by the NVIDIA Deep Learning Data Synthesizer intended for use in object detection, pose estimation, and tracking applications. This dataset contains 144k stereo image pairs that synthetically combine 18 camera viewpoints of three photorealistic virtual environments with up to 10 objects (chosen randomly from the 21 object models of the YCB dataset) and flying distractors. Source: SIDOD: A Synthetic Image Dataset for 3D Object Pose Recognition with Distractors Image Source: https://research.nvidia.com/publication/2019-06_SIDOD%3A-A-Synthetic

SidechainNet is a protein structure prediction dataset that directly extends ProteinNet. Specifically, SidechainNet adds measurements for protein angles and coordinates that describe the complete, all-atom protein structure (backbone and sidechain, excluding hydrogens) instead of the protein backbone alone. Source: https://github.com/jonathanking/sidechainnet

This data contains the election polls for the 2004, 2008, 2012, and 2016 US presidential election by state including data on undecided voter proportions. See https://github.com/bonStats/undecided-voters-us-pres-elections#readme for data description.

Drug side effects and drug-drug interactions were mined from publicly available data. Offsides is a database of drug side-effects that were found, but are not listed on the official FDA label. Twosides is the only comprehensive database drug-drug-effect relationships. Over 3,300 drugs and 63,000 combinations connected to millions of potential adverse reactions. Source: http://tatonettilab.org/offsides/ Image Source: http://doi.org/10.1126/scitranslmed.3003377

Description: <a href="https://gts.ai/dataset-download/side-profile-tires-dataset/" target="_blank">👉 Download the dataset here</a> This dataset consists of meticulously annotated images of tire side profiles, specifically designed for image segmentation tasks. Each tire has been manually labeled to ensure high accuracy, making this dataset ideal for training machine learning models focused on tire detection, classification, or related automotive applications. The annotations are provided in the YOLO v5 format, leveraging the PyTorch framework for deep learning applications. The dataset offers a robust foundation for researchers and developers working on object detection, autonomous vehicles, quality control, or any project requiring precise tire identification from images. Download Dataset Data Collection and Labeling Process: Manual Labeling: Every tire in the dataset has been individually labeled to guarantee that the annotations are highly precise, significantly reducing the margin of error in model training. Annotation Format: YOLO v5 PyTorch format, a highly efficient and widely used format for real-time object detection systems. Pre-processing Applied: Auto-orientation: Pixel data has been automatically oriented, and EXIF orientation metadata has been stripped to ensure uniformity across all images, eliminating issues related to image orientation during processing. Resizing: All images have been resized to 416×416 pixels using stretching to maintain compatibility with common object detection frameworks like YOLO. This resizing standardizes the image input size while preserving visual integrity. Applications: Automotive Industry: This dataset is suitable for automotive-focused AI models, including tire quality assessment, tread pattern recognition, and autonomous vehicle systems. Surveillance and Security: Use cases in monitoring systems where identifying tires is crucial for vehicle recognition in parking lots or traffic management systems. Manufacturing and Quality Control: Can be used in tire manufacturing processes to automate defect detection and classification. Dataset Composition: Number of Images: [Add specific number] File Format: JPEG/PNG Annotation Format: YOLO v5 PyTorch Image Size: 416×416 (standardized across all images) This dataset is sourced from Kaggle.