Common corruptions dataset for CIFAR10
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Dataset Introduction
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Enlarge the dataset to understand how image background effect the Computer Vision ML model. With the following topics: Blur Background / Segmented Background / AI generated Background/ Bias of tools during annotation/ Color in Background / Dependent Factor in Background/ LatenSpace Distance of Foreground/ Random Background with Real Environment!
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The ArtiFact dataset is a large-scale image dataset that aims to include a diverse collection of real and synthetic images from multiple categories, including Human/Human Faces, Animal/Animal Faces, Places, Vehicles, Art, and many other real-life objects. The dataset comprises 8 sources that were carefully chosen to ensure diversity and includes images synthesized from 25 distinct methods, including 13 GANs, 7 Diffusion, and 5 other miscellaneous generators. The dataset contains 2,496,738 images, comprising 964,989 real images and 1,531,749 fake images.
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Abstract The classification and recognition of foliar diseases is an increasingly developing field of research, where the concepts of machine and deep learning are used to support agricultural stakeholders. Datasets are the fuel for the development of these technologies. In this paper, we release and make publicly available the field dataset collected to diagnose and monitor plant symptoms, called DiaMOS Plant, consisting of 3505 images of pear fruit and leaves affected by four diseases. In addition, we perform a comparative analysis of existing literature datasets designed for the classification and recognition of leaf diseases, highlighting the main features that maximize the value and information content of the collected data. This study provides guidelines that will be useful to the research community in the context of the selection and construction of datasets.
The RITE (Retinal Images vessel Tree Extraction) is a database that enables comparative studies on segmentation or classification of arteries and veins on retinal fundus images, which is established based on the public available DRIVE database (Digital Retinal Images for Vessel Extraction).
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For a detailed description, we refer to Section 3 in our research article.
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CWD30 comprises over 219,770 high-resolution images of 20 weed species and 10 crop species, encompassing various growth stages, multiple viewing angles, and environmental conditions. The images were collected from diverse agricultural fields across different geographic locations and seasons, ensuring a representative dataset.
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DeepPCB
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We provide multiple human annotations for each test image in Fashion-MNIST. This can be used as soft labels or probabilistic labels instead of the usual hard (single) labels.
We introduce the Oracle-MNIST dataset, comprising of 2828 grayscale images of 30,222 ancient characters from 10 categories, for benchmarking pattern classification, with particular challenges on image noise and distortion. The training set totally consists of 27,222 images, and the test set contains 300 images per class. Oracle-MNIST shares the same data format with the original MNIST dataset, allowing for direct compatibility with all existing classifiers and systems, but it constitutes a more challenging classification task than MNIST. The images of ancient characters suffer from 1) extremely serious and unique noises caused by three-thousand years of burial and aging and 2) dramatically variant writing styles by ancient Chinese, which all make them realistic for machine learning research. The dataset is freely available at https://github.com/wm-bupt/oracle-mnist.
This database is a database of backdoored neural networks intended for face recognition. The networks are of the FaceNet architecture and are trained on Casia-WebFace, with and without additional samples (which are the source of the backdoor). More information regarding backdoors and the project within which this fits can be found in the public release of the source code : https://gitlab.idiap.ch/bob/bob.paper.backdoored_facenets.biosig2022.
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Several datasets are fostering innovation in higher-level functions for everyone, everywhere. By providing this repository, we hope to encourage the research community to focus on hard problems. In this repository, we present the real results severity (BIRADS) and pathology (post-report) classifications provided by the Radiologist Director from the Radiology Department of Hospital Fernando Fonseca while diagnosing several patients (see dataset-uta4-dicom) from our User Tests and Analysis 4 (UTA4) study. Here, we provide a dataset for the measurements of both severity (BIRADS) and pathology classifications concerning the patient diagnostic. Work and results are published on a top Human-Computer Interaction (HCI) conference named AVI 2020 (page). Results were analyzed and interpreted from our Statistical Analysis charts. The user tests were made in clinical institutions, where clinicians diagnose several patients for a Single-Modality vs Multi-Modality comparison. For example, in these t
Histological images of colorectal cancer, derived from the TCGA database
Digitally Generated Numerals (DIGITal) Description The Digitally Generated Numerals (DIGITal) dataset consists of 100,000 image pairs representing digits from 0 to 9. These image pairs include both low and high-quality versions, with a resolution of 128x128 pixels.
DeepGraviLens is a data set of simulated gravitational lenses consisting of images associated with brightness variation time series. In this dataset, both non-transient and transient phenomena (supernovae explosions) are simulated.
FractureAtlas is a musculoskeletal bone fracture dataset with annotations for deep learning tasks like classification, localization, and segmentation. The dataset contains a total of 4,083 X-Ray images with annotation in COCO, VGG, YOLO, and Pascal VOC format. This dataset is made freely available for any purpose. The data provided within this work are free to copy, share or redistribute in any medium or format. The data might be adapted, remixed, transformed, and built upon. The dataset is licensed under a CC-BY 4.0 license. It should be noted that to use the dataset correctly, one needs to have knowledge of medical and radiology fields to understand the results and make conclusions based on the dataset. It's also important to consider the possibility of labeling errors.
HOWS-CL-25 (Household Objects Within Simulation dataset for Continual Learning) is a synthetic dataset especially designed for object classification on mobile robots operating in a changing environment (like a household), where it is important to learn new, never seen objects on the fly. This dataset can also be used for other learning use-cases, like instance segmentation or depth estimation. Or where household objects or continual learning are of interest.
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The HRPlanesv2 dataset contains 2120 VHR Google Earth images. To further improve experiment results, images of airports from many different regions with various uses (civil/military/joint) selected and labeled. A total of 14,335 aircrafts have been labelled. Each image is stored as a ".jpg" file of size 4800 x 2703 pixels and each label is stored as YOLO ".txt" format. Dataset has been split in three parts as 70% train, %20 validation and test. The aircrafts in the images in the train and validation datasets have a percentage of 80 or more in size. Link: https://github.com/dilsadunsal/HRPlanesv2-Data-Set
The IRFL dataset consists of idioms, similes, and metaphors with matching figurative and literal images, as well as two novel tasks of multimodal figurative understanding and preference.
Dataset can be used by anyone who is interested to perform morphological classification of galaxies. Originally dataset provided by Kaggle user Jay Lin (https://www.kaggle.com/jay1985) 4 years ago. Dataset was used in conference paper "Morphological Classification of Galaxies Using SpinalNet"
Onchocerciasis is causing blindness in over half a million people in the world today. Drug development for the disease is crippled as there is no way of measuring effectiveness of the drug without an invasive procedure. Drug efficacy measurement through assessment of viability of onchocerca worms requires the patients to undergo nodulectomy which is invasive, expensive, time-consuming, skill-dependent, infrastructure dependent and lengthy process.
The data used in - "Radio Galaxy Zoo EMU: Towards a Semantic Radio Galaxy Morphology Taxonomy" (Bowles et al. submitted) - "A New Task: Deriving Semantic Class Targets for the Physical Sciences" (Bowles et al. 2022: https://arxiv.org/abs/2210.14760) accepted at the Fifth Workshop on Machine Learning and the Physical Sciences, Neural Information Processing Systems 2022.
Raw-Microscopy:
This dataset was acquired in a retrospective study from a cohort of pediatric patients admitted with abdominal pain to Children’s Hospital St. Hedwig in Regensburg, Germany. Multiple abdominal B-mode ultrasound images were acquired for most patients, with the number of views varying from 1 to 15. The images depict various regions of interest, such as the abdomen’s right lower quadrant, appendix, intestines, lymph nodes and reproductive organs. Alongside multiple US images for each subject, the dataset includes information encompassing laboratory tests, physical examination results, clinical scores, such as Alvarado and pediatric appendicitis scores, and expert-produced ultrasonographic findings. Lastly, the subjects were labeled w.r.t. three target variables: diagnosis (appendicitis vs. no appendicitis), management (surgical vs. conservative) and severity (complicated vs. uncomplicated or no appendicitis). The study was approved by the Ethics Committee of the University of Regensburg (
ALFI (Annotations for Label-Free Images) is a dataset of images and annotations for label-free microscopy imaging. It consists of 29 time-lapse image sequences with various annotations (pixel-wise segmentation masks, object-wise bounding boxes, and tracking information), made publicly available to the scientific community through figshare.
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A fundamental component of human vision is our ability to parse complex visual scenes and judge the relations between their constituent objects. AI benchmarks for visual reasoning have driven rapid progress in recent years with state-of-the-art systems now reaching human accuracy on some of these benchmarks. Yet, there remains a major gap between humans and AI systems in terms of the sample efficiency with which they learn new visual reasoning tasks. Humans' remarkable efficiency at learning has been at least partially attributed to their ability to harness compositionality -- allowing them to efficiently take advantage of previously gained knowledge when learning new tasks. Here, we introduce a novel visual reasoning benchmark, Compositional Visual Relations (CVR), to drive progress towards the development of more data-efficient learning algorithms. We take inspiration from fluidic intelligence and non-verbal reasoning tests and describe a novel method for creating compositions of abs
Mudestreda Multimodal Device State Recognition Dataset obtained from real industrial milling device with Time Series and Image Data for Classification, Regression, Anomaly Detection, Remaining Useful Life (RUL) estimation, Signal Drift measurement, Zero Shot Flank Took Wear, and Feature Engineering purposes.