The CIFAR-100 dataset (Canadian Institute for Advanced Research, 100 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. There are 600 images per class. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs). There are 500 training images and 100 testing images per class.
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The Reddit dataset is a graph dataset from Reddit posts made in the month of September, 2014. The node label in this case is the community, or “subreddit”, that a post belongs to. 50 large communities have been sampled to build a post-to-post graph, connecting posts if the same user comments on both. In total this dataset contains 232,965 posts with an average degree of 492. The first 20 days are used for training and the remaining days for testing (with 30% used for validation). For features, off-the-shelf 300-dimensional GloVe CommonCrawl word vectors are used.
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Common corruptions dataset for CIFAR10
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The Schema-Guided Dialogue (SGD) dataset consists of over 20k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 20 domains, ranging from banks and events to media, calendar, travel, and weather. For most of these domains, the dataset contains multiple different APIs, many of which have overlapping functionalities but different interfaces, which reflects common real-world scenarios. The wide range of available annotations can be used for intent prediction, slot filling, dialogue state tracking, policy imitation learning, language generation, user simulation learning, among other tasks in large-scale virtual assistants. Besides these, the dataset has unseen domains and services in the evaluation set to quantify the performance in zero-shot or few shot settings.
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The Neuromorphic-Caltech101 (N-Caltech101) dataset is a spiking version of the original frame-based Caltech101 dataset. The original dataset contained both a "Faces" and "Faces Easy" class, with each consisting of different versions of the same images. The "Faces" class has been removed from N-Caltech101 to avoid confusion, leaving 100 object classes plus a background class. The N-Caltech101 dataset was captured by mounting the ATIS sensor on a motorized pan-tilt unit and having the sensor move while it views Caltech101 examples on an LCD monitor as shown in the video below. A full description of the dataset and how it was created can be found in the paper below. Please cite this paper if you make use of the dataset.
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CIFAR-10H is a new dataset of soft labels reflecting human perceptual uncertainty for the 10,000-image CIFAR-10 test set. This contains 1,000 images for each of the 10 categories in the original CIFAR-10 dataset.
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Data Set Information: Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))
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The HRF dataset is a dataset for retinal vessel segmentation which comprises 45 images and is organized as 15 subsets. Each subset contains one healthy fundus image, one image of patient with diabetic retinopathy and one glaucoma image. The image sizes are 3,304 x 2,336, with a training/testing image split of 22/23.
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A large real-world event-based dataset for object classification.
he RSSCN7 dataset contains satellite images acquired from Google Earth, which is originally collected for remote sensing scene classification. We conduct image synthesis on RSSCN7 to make it capable of the image inpainting task. It has seven classes: grassland, farmland, industrial and commercial regions, river and lake, forest field, residential region, and parking lot. Each class has 400 images, so there are total 2,800 images in the RSSCN7 dataset.
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A team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh along with their collaborators from Pakistan and Malaysia in collaboration with medical doctors have created a database of chest X-ray images for COVID-19 positive cases along with Normal and Viral Pneumonia images. This COVID-19, normal, and other lung infection dataset is released in stages. In the first release, we have released 219 COVID-19, 1341 normal, and 1345 viral pneumonia chest X-ray (CXR) images. In the first update, we have increased the COVID-19 class to 1200 CXR images. In the 2nd update, we have increased the database to 3616 COVID-19 positive cases along with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images and corresponding lung masks. We will continue to update this database as soon as we have new x-ray images for COVID-19 pneumonia patients.
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The purpose of this dataset was to study gender bias in occupations. Online biographies, written in English, were collected to find the names, pronouns, and occupations. Twenty-eight most frequent occupations were identified based on their appearances. The resulting dataset consists of 397,340 biographies spanning twenty-eight different occupations. Of these occupations, the professor is the most frequent, with 118,400 biographies, while the rapper is the least frequent, with 1,406 biographies. Important information about the biographies: 1. The longest biography is 194 tokens, while the shortest is eighteen; the median biography length is seventy-two tokens. 2. It should be noted that the demographics of online biographies’ subjects differ from those of the overall workforce and that this dataset does not contain all biographies on the Internet.
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Dataset Introduction
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The N-ImageNet dataset is an event-camera counterpart for the ImageNet dataset. The dataset is obtained by moving an event camera around a monitor displaying images from ImageNet. N-ImageNet contains approximately 1,300k training samples and 50k validation samples. In addition, the dataset also contains variants of the validation dataset recorded under a wide range of lighting or camera trajectories. Additional details about the dataset are explained in the paper available through this link. Please cite this paper if you make use of the dataset.
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Data was collected for normal bearings, single-point drive end and fan end defects. Data was collected at 12,000 samples/second and at 48,000 samples/second for drive end bearing experiments. All fan end bearing data was collected at 12,000 samples/second.
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The goal for ISIC 2019 is classify dermoscopic images among nine different diagnostic categories.25,331 images are available for training across 8 different categories. Two tasks will be available for participation: 1) classify dermoscopic images without meta-data, and 2) classify images with additional available meta-data.
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The SD-198 dataset contains 198 different diseases from different types of eczema, acne and various cancerous conditions. There are 6,584 images in total. A subset include the classes with more than 20 image samples, namely SD-128."
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SciRepEval is a comprehensive benchmark for training and evaluating scientific document representations. It includes 25 challenging and realistic tasks, 11 of which are new, across four formats: classification, regression, ranking and search.
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This dataset is a combination of the following three datasets : figshare, SARTAJ dataset and Br35H
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A public data set of walking full-body kinematics and kinetics in individuals with Parkinson’s disease
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The SSC dataset is a spiking version of the Speech Commands dataset release by Google (Speech Commands). SSC was generated using Lauscher, an artificial cochlea model. The SSC dataset consists of utterances recorded from a larger number of speakers under controlled conditions. Spikes were generated in 700 input channels, and it contains 35 word categories from a large number of speakers.
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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This dataset is described in the ALTA 2021 Shared Task website and associated CodaLab competition.
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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.
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.
Digital radiography is widely available and the standard modality in trauma imaging, often enabling to diagnose pediatric wrist fractures. However, image interpretation requires time-consuming specialized training. Due to astonishing progress in computer vision algorithms, automated fracture detection has become a topic of research interest. This paper presents the GRAZPEDWRI-DX dataset containing annotated pediatric trauma wrist radiographs of 6,091 patients, treated at the Department for Pediatric Surgery of the University Hospital Graz between 2008 and 2018. A total number of 10,643 studies (20,327 images) are made available, typically covering posteroanterior and lateral projections. The dataset is annotated with 74,459 image tags and features 67,771 labeled objects. We de-identified all radiographs and converted the DICOM pixel data to 16-Bit grayscale PNG images. The filenames and the accompanying text files provide basic patient information (age, sex). Several pediatric radiolog
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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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Sentiment140 is a dataset that allows you to discover the sentiment of a brand, product, or topic on Twitter.
This dataset is a collection of labelled PCAP files, both encrypted and unencrypted, across 10 applications, as well as a pandas dataframe in HDF5 format containing detailed metadata summarizing the connections from those files. It was created to assist the development of machine learning tools that would allow operators to see the traffic categories of both encrypted and unencrypted traffic flows. In particular, features of the network packet traffic timing and size information (both inside of and outside of the VPN) can be leveraged to predict the application category that generated the traffic.
For a detailed description, we refer to Section 3 in our research article.
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This brain tumor dataset contains 3064 T1-weighted contrast-enhanced images with three kinds of brain tumor. Detailed information on the dataset can be found in the readme file.
We construct the ForgeryNet dataset, an extremely large face forgery dataset with unified annotations in image- and video-level data across four tasks: 1) Image Forgery Classification, including two-way (real / fake), three-way (real / fake with identity-replaced forgery approaches / fake with identity-remained forgery approaches), and n-way (real and 15 respective forgery approaches) classification. 2) Spatial Forgery Localization, which segments the manipulated area of fake images compared to their corresponding source real images. 3) Video Forgery Classification, which re-defines the video-level forgery classification with manipulated frames in random positions. This task is important because attackers in real world are free to manipulate any target frame. and 4) Temporal Forgery Localization, to localize the temporal segments which are manipulated. ForgeryNet is by far the largest publicly available deep face forgery dataset in terms of data-scale (2.9 million images, 221,247 video
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Open Dataset: Mobility Scenario FIMU
Huggingface Datasets is a great library, but it lacks standardization, and datasets require preprocessing work to be used interchangeably. tasksource automates this and facilitates reproducible multi-task learning scaling.
We present XHate-999, a multi-domain and multilingual evaluation data set for abusive language detection. By aligning test instances across six typologically diverse languages, XHate-999 for the first time allows for disentanglement of the domain transfer and language transfer effects in abusive language detection. We conduct a series of domain- and language-transfer experiments with state-of-the-art monolingual and multilingual transformer models, setting strong baseline results and profiling XHate-999 as a comprehensive evaluation resource for abusive language detection. Finally, we show that domain- and language-adaption, via intermediate masked language modeling on abusive corpora in the target language, can lead to substantially improved abusive language detection in the target language in the zero-shot transfer setups.
Attention Deficit Hyperactivity Disorder (ADHD) affects at least 5-10% of school-age children and is associated with substantial lifelong impairment, with annual direct costs exceeding $36 billion/year in the US. Despite a voluminous empirical literature, the scientific community remains without a comprehensive model of the pathophysiology of ADHD. Further, the clinical community remains without objective biological tools capable of informing the diagnosis of ADHD for an individual or guiding clinicians in their decision-making regarding treatment.
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The quality of AI-generated images has rapidly increased, leading to concerns of authenticity and trustworthiness.
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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.
DeepPCB
The data set covers recordings of ripening fruit with labels of destructive measurements (fruit flesh firmness, sugar content and overall ripeness). The labels are provided within three categories (firmness, sweetness and overall ripeness). Four measurement series were performed. Besides 1018 labeled recordings, the data set contains 4671 recordings without ripeness label.
The dermatology differential diagnoses (ddx) dataset for skin condition classification includes expert annotations and model predictions for 1947 cases. Note that no images or meta information are provided. The expert annotations come in the form of differential diagnoses, i.e., partial rankings of conditions, and there is a high level of disagreement among experts, making this a perfect benchmark for dealing with disagreement. The data has been introduced in [1] and [2].
Extended Agriculture-Vision dataset comprises two parts:
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.
Hephaestus is the first large-scale InSAR dataset. Driven by volcanic unrest detection, it provides 19,919 unique satellite frames annotated with a diverse set of labels. Moreover, each sample is accompanied by a textual description of its contents. The goal of this dataset is to boost research on exploitation of interferometric data enabling the application of state-of-the-art computer vision+NLP methods. Furthermore, the annotated dataset is bundled with a large archive of unlabeled frames to enable large-scale self-supervised learning. The final size of the dataset amounts to 110,573 interferograms.
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.
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LEPISZCZE is an open-source comprehensive benchmark for Polish NLP and a continuous-submission leaderboard, concentrating public Polish datasets (existing and new) in specific tasks.
LLeQA is a French native dataset for studying information retrieval and long-form question answering in the legal domain. It consists of a knowledge corpus of 27,941 statutory articles collected from the Belgian legislation, and 1,868 legal questions posed by Belgian citizens and labeled by experienced jurists with a comprehensive answer rooted in relevant articles from the corpus.