100+ datasets found
  1. cats-image

    • huggingface.co
    Updated Apr 23, 2022
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    Hugging Face (2022). cats-image [Dataset]. https://huggingface.co/datasets/huggingface/cats-image
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    Dataset updated
    Apr 23, 2022
    Dataset authored and provided by
    Hugging Facehttps://huggingface.co/
    Description

    huggingface/cats-image dataset hosted on Hugging Face and contributed by the HF Datasets community

  2. cats_vs_dogs

    • huggingface.co
    • tensorflow.org
    • +1more
    Updated May 23, 2024
    + more versions
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    Microsoft (2024). cats_vs_dogs [Dataset]. https://huggingface.co/datasets/microsoft/cats_vs_dogs
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 23, 2024
    Dataset authored and provided by
    Microsofthttp://microsoft.com/
    License

    https://choosealicense.com/licenses/unknown/https://choosealicense.com/licenses/unknown/

    Description

    Dataset Card for Cats Vs. Dogs

      Dataset Summary
    

    A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset. From the competition page:

    The Asirra data set Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a CAPTCHA… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/cats_vs_dogs.

  3. Animal Image Dataset - Cats, Dogs, and Foxes

    • kaggle.com
    Updated Nov 5, 2024
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    mahsa sanaei (2024). Animal Image Dataset - Cats, Dogs, and Foxes [Dataset]. https://www.kaggle.com/datasets/snmahsa/animal-image-dataset-cats-dogs-and-foxes
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 5, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    mahsa sanaei
    Description

    This dataset contains 300 images of three types of animals: cats, dogs, and foxes. Each category has 100 pictures. The images can help in learning about animals and building computer programs that recognize them. You can use this dataset for school projects, studies in artificial intelligence, or just to learn more about these animals.

  4. R

    Cats Dataset

    • universe.roboflow.com
    zip
    Updated Nov 17, 2022
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    Mohamed Traore (2022). Cats Dataset [Dataset]. https://universe.roboflow.com/mohamed-traore-2ekkp/cats-n9b87/dataset/3
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    zipAvailable download formats
    Dataset updated
    Nov 17, 2022
    Dataset authored and provided by
    Mohamed Traore
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Cats Bounding Boxes
    Description

    About this Dataset

    This dataset was created by exporting the Oxford Pets dataset from Roboflow Universe, generating a version with Modify Classes to drop all of the classes for the labeled dog breeds and consolidating all cat breeds under the label, "cat." The bounding boxes were also modified to incude the entirety of the cats within the images, rather than only their faces/heads.

    https://i.imgur.com/3IEzlCf.png" alt="Annotated image of a cat from the dataset">

    Oxford Pets

    • The Oxford Pets dataset (also known as the "dogs vs cats" dataset) is a collection of images and annotations labeling various breeds of dogs and cats. There are approximately 100 examples of each of the 37 breeds. This dataset contains the object detection portion of the original dataset with bounding boxes around the animals' heads.

    • Origin: This dataset was collected by the Visual Geometry Group (VGG) at the University of Oxford.

  5. R

    Gray Cats Dataset

    • universe.roboflow.com
    zip
    Updated Dec 1, 2024
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    cats (2024). Gray Cats Dataset [Dataset]. https://universe.roboflow.com/cats-ewekp/white-gray-cats
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    zipAvailable download formats
    Dataset updated
    Dec 1, 2024
    Dataset authored and provided by
    cats
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Cats Bounding Boxes
    Description

    Gray Cats

    ## Overview
    
    Gray Cats is a dataset for object detection tasks - it contains Cats annotations for 985 images.
    
    ## Getting Started
    
    You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
    
      ## License
    
      This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
    
  6. R

    Coco 2017 Cats Dataset

    • universe.roboflow.com
    zip
    Updated Nov 21, 2021
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    Breno Aquino (2021). Coco 2017 Cats Dataset [Dataset]. https://universe.roboflow.com/breno-aquino/coco-2017-cats/dataset/1
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    zipAvailable download formats
    Dataset updated
    Nov 21, 2021
    Dataset authored and provided by
    Breno Aquino
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Cats Bounding Boxes
    Description

    COCO 2017 Cats

    ## Overview
    
    COCO 2017 Cats is a dataset for object detection tasks - it contains Cats annotations for 4,112 images.
    
    ## Getting Started
    
    You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
    
      ## License
    
      This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
    
  7. h

    DALL-E-Cats

    • huggingface.co
    Updated Sep 7, 2023
    + more versions
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    BirdL Legacy (2023). DALL-E-Cats [Dataset]. https://huggingface.co/datasets/TheBirdLegacy/DALL-E-Cats
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 7, 2023
    Dataset authored and provided by
    BirdL Legacy
    License

    https://choosealicense.com/licenses/other/https://choosealicense.com/licenses/other/

    Description

    DALL-E-Cats is a dataset meant to produce a synthetic animal dataset. This is a successor to DALL-E-Dogs. DALL-E-Dogs and DALL-E-Cats will be fed into an image classifier to see how it performs. This is under the BirdL-AirL License.

  8. Cookie Cats

    • kaggle.com
    Updated Aug 5, 2020
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    Arpit Dwivedi (2020). Cookie Cats [Dataset]. https://www.kaggle.com/datasets/arpitdw/cokie-cats
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 5, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Arpit Dwivedi
    Description

    Dataset

    This dataset was created by Arpit Dwivedi

    Contents

  9. R

    Cats And Dogs Dataset

    • universe.roboflow.com
    zip
    Updated Jul 28, 2025
    + more versions
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    Purbayan (2025). Cats And Dogs Dataset [Dataset]. https://universe.roboflow.com/purbayan/cats-and-dogs-siq4y/model/1
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    zipAvailable download formats
    Dataset updated
    Jul 28, 2025
    Dataset authored and provided by
    Purbayan
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Variables measured
    Cats And Dogs Bounding Boxes
    Description

    Cats And Dogs

    ## Overview
    
    Cats And Dogs is a dataset for object detection tasks - it contains Cats And Dogs annotations for 1,994 images.
    
    ## Getting Started
    
    You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
    
      ## License
    
      This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).
    
  10. h

    cats

    • huggingface.co
    Updated Aug 16, 2022
    + more versions
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    HugGAN Community (2022). cats [Dataset]. https://huggingface.co/datasets/huggan/cats
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 16, 2022
    Dataset authored and provided by
    HugGAN Community
    Description

    huggan/cats dataset hosted on Hugging Face and contributed by the HF Datasets community

  11. z

    Controlled Anomalies Time Series (CATS) Dataset

    • zenodo.org
    bin, csv
    Updated Jul 11, 2024
    + more versions
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    Patrick Fleith; Patrick Fleith (2024). Controlled Anomalies Time Series (CATS) Dataset [Dataset]. http://doi.org/10.5281/zenodo.8338435
    Explore at:
    csv, binAvailable download formats
    Dataset updated
    Jul 11, 2024
    Dataset provided by
    Solenix Engineering GmbH
    Authors
    Patrick Fleith; Patrick Fleith
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The Controlled Anomalies Time Series (CATS) Dataset consists of commands, external stimuli, and telemetry readings of a simulated complex dynamical system with 200 injected anomalies.

    The CATS Dataset exhibits a set of desirable properties that make it very suitable for benchmarking Anomaly Detection Algorithms in Multivariate Time Series [1]:

    • Multivariate (17 variables) including sensors reading and control signals. It simulates the operational behaviour of an arbitrary complex system including:
      • 4 Deliberate Actuations / Control Commands sent by a simulated operator / controller, for instance, commands of an operator to turn ON/OFF some equipment.
      • 3 Environmental Stimuli / External Forces acting on the system and affecting its behaviour, for instance, the wind affecting the orientation of a large ground antenna.
      • 10 Telemetry Readings representing the observable states of the complex system by means of sensors, for instance, a position, a temperature, a pressure, a voltage, current, humidity, velocity, acceleration, etc.
    • 5 million timestamps. Sensors readings are at 1Hz sampling frequency.
      • 1 million nominal observations (the first 1 million datapoints). This is suitable to start learning the "normal" behaviour.
      • 4 million observations that include both nominal and anomalous segments. This is suitable to evaluate both semi-supervised approaches (novelty detection) as well as unsupervised approaches (outlier detection).
    • 200 anomalous segments. One anomalous segment may contain several successive anomalous observations / timestamps. Only the last 4 million observations contain anomalous segments.
    • Different types of anomalies to understand what anomaly types can be detected by different approaches. The categories are available in the dataset and in the metadata.
    • Fine control over ground truth. As this is a simulated system with deliberate anomaly injection, the start and end time of the anomalous behaviour is known very precisely. In contrast to real world datasets, there is no risk that the ground truth contains mislabelled segments which is often the case for real data.
    • Suitable for root cause analysis. In addition to the anomaly category, the time series channel in which the anomaly first developed itself is recorded and made available as part of the metadata. This can be useful to evaluate the performance of algorithm to trace back anomalies to the right root cause channel.
    • Affected channels. In addition to the knowledge of the root cause channel in which the anomaly first developed itself, we provide information of channels possibly affected by the anomaly. This can also be useful to evaluate the explainability of anomaly detection systems which may point out to the anomalous channels (root cause and affected).
    • Obvious anomalies. The simulated anomalies have been designed to be "easy" to be detected for human eyes (i.e., there are very large spikes or oscillations), hence also detectable for most algorithms. It makes this synthetic dataset useful for screening tasks (i.e., to eliminate algorithms that are not capable to detect those obvious anomalies). However, during our initial experiments, the dataset turned out to be challenging enough even for state-of-the-art anomaly detection approaches, making it suitable also for regular benchmark studies.
    • Context provided. Some variables can only be considered anomalous in relation to other behaviours. A typical example consists of a light and switch pair. The light being either on or off is nominal, the same goes for the switch, but having the switch on and the light off shall be considered anomalous. In the CATS dataset, users can choose (or not) to use the available context, and external stimuli, to test the usefulness of the context for detecting anomalies in this simulation.
    • Pure signal ideal for robustness-to-noise analysis. The simulated signals are provided without noise: while this may seem unrealistic at first, it is an advantage since users of the dataset can decide to add on top of the provided series any type of noise and choose an amplitude. This makes it well suited to test how sensitive and robust detection algorithms are against various levels of noise.
    • No missing data. You can drop whatever data you want to assess the impact of missing values on your detector with respect to a clean baseline.

    Change Log

    Version 2

    • Metadata: we include a metadata.csv with information about:
      • Anomaly categories
      • Root cause channel (signal in which the anomaly is first visible)
      • Affected channel (signal in which the anomaly might propagate) through coupled system dynamics
    • Removal of anomaly overlaps: version 1 contained anomalies which overlapped with each other resulting in only 190 distinct anomalous segments. Now, there are no more anomaly overlaps.
    • Two data files: CSV and parquet for convenience.

    [1] Example Benchmark of Anomaly Detection in Time Series: “Sebastian Schmidl, Phillip Wenig, and Thorsten Papenbrock. Anomaly Detection in Time Series: A Comprehensive Evaluation. PVLDB, 15(9): 1779 - 1797, 2022. doi:10.14778/3538598.3538602”

    About Solenix

    Solenix is an international company providing software engineering, consulting services and software products for the space market. Solenix is a dynamic company that brings innovative technologies and concepts to the aerospace market, keeping up to date with technical advancements and actively promoting spin-in and spin-out technology activities. We combine modern solutions which complement conventional practices. We aspire to achieve maximum customer satisfaction by fostering collaboration, constructivism, and flexibility.

  12. e

    Cats per square kilometre- upper 95th percentile

    • data.europa.eu
    • cloud.csiss.gmu.edu
    • +2more
    csv
    Updated Nov 2, 2023
    + more versions
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    Animal and Plant Health Agency (2023). Cats per square kilometre- upper 95th percentile [Dataset]. https://data.europa.eu/data/datasets/cats-per-square-kilometre-upper-95th-percentile?locale=en
    Explore at:
    csvAvailable download formats
    Dataset updated
    Nov 2, 2023
    Dataset authored and provided by
    Animal and Plant Health Agency
    Description

    This dataset is a modelled dataset, describing an upper estimate of cats per square kilometre across GB. The figures are aligned to the British national grid, with a population estimate provided for each 1km square. These data were generated as part of the delivery of commissioned research. The data contained within this dataset are modelled figures, based on upper 95th percentile national estimates for pet population, and available information on Veterinary activity across GB. The data are accurate as of 01/01/2015. The data provided are summarised to the 1km level. Further information on this research is available in a research publication by James Aegerter, David Fouracre & Graham C. Smith, discussing the structure and density of pet cat and dog populations across Great Britain. Attribution statement: ©Crown Copyright, APHA 2016

  13. Cat Dataset

    • kaggle.com
    Updated Mar 9, 2025
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    samuel ayman (2025). Cat Dataset [Dataset]. https://www.kaggle.com/datasets/samuelayman/cat-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 9, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    samuel ayman
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    This Kaggle dataset includes 1000 JPEG images of diverse cats, paired with 1000 text files containing bounding box annotations in YOLO format. It's designed for training object detection models to recognize cats, and is suitable for various computer vision applications and educational use

  14. h

    Cat_and_Dog

    • huggingface.co
    Updated Oct 2, 2023
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    Dowon Hwang (2023). Cat_and_Dog [Dataset]. https://huggingface.co/datasets/Bingsu/Cat_and_Dog
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 2, 2023
    Authors
    Dowon Hwang
    License

    https://choosealicense.com/licenses/cc0-1.0/https://choosealicense.com/licenses/cc0-1.0/

    Description

    Dataset Summary

    A dataset from kaggle with duplicate data removed.

      Data Fields
    

    The data instances have the following fields:

    image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"]… See the full description on the dataset page: https://huggingface.co/datasets/Bingsu/Cat_and_Dog.

  15. CATS-ISS Level 2 Operational Day Mode 7.2 Version 3-01 5 km Profile -...

    • data.nasa.gov
    • data.staging.idas-ds1.appdat.jsc.nasa.gov
    Updated Apr 1, 2025
    + more versions
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    nasa.gov (2025). CATS-ISS Level 2 Operational Day Mode 7.2 Version 3-01 5 km Profile - Dataset - NASA Open Data Portal [Dataset]. https://data.nasa.gov/dataset/cats-iss-level-2-operational-day-mode-7-2-version-3-01-5-km-profile-8cc50
    Explore at:
    Dataset updated
    Apr 1, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    CATS-ISS_L2O_D-M7.2-V3-01_05kmPro is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Day Mode 7.2 Version 3-01 5 km Profile data product. This collection spans from March 25, 2015 to October 29, 2017. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data products contain geophysical parameters and are derived from Level 1 data, at 60m vertical and 5km horizontal resolution.

  16. R

    Cats And Dogs Image Classification Dataset

    • universe.roboflow.com
    zip
    Updated Mar 1, 2023
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    Workspace1 (2023). Cats And Dogs Image Classification Dataset [Dataset]. https://universe.roboflow.com/workspace1-aalti/cats-and-dogs-image-classification
    Explore at:
    zipAvailable download formats
    Dataset updated
    Mar 1, 2023
    Dataset authored and provided by
    Workspace1
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Cats And Dogs
    Description

    Cats And Dogs Image Classification

    ## Overview
    
    Cats And Dogs Image Classification is a dataset for classification tasks - it contains Cats And Dogs annotations for 2,000 images.
    
    ## Getting Started
    
    You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
    
      ## License
    
      This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
    
  17. Number of cats in Sweden 2010-2024

    • statista.com
    Updated Jul 23, 2025
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    Statista (2025). Number of cats in Sweden 2010-2024 [Dataset]. https://www.statista.com/statistics/516033/cat-population-europe-sweden/
    Explore at:
    Dataset updated
    Jul 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Sweden
    Description

    The estimated number of cats owned by households in Sweden increased in selected years from 2010 to 2024. The cat population in Sweden was measured at approximately **** million in 2024, an increase from the previous year.

  18. Cats from memes

    • kaggle.com
    Updated Jul 16, 2024
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    vekosek3000 (2024). Cats from memes [Dataset]. https://www.kaggle.com/datasets/vekosek/cats-from-memes
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 16, 2024
    Dataset provided by
    Kaggle
    Authors
    vekosek3000
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    In this dataset, I collected cat faces from memes. I tried to take photos with only one cat and without labels (but you can find some images with it). The dataset also has some ordinary photos of cats. You can try to create a GAN or diffuser model to generate memes or use this dataset as you like :)

    It's my first dataset so I will be grateful if you suggest how to upgrade it

  19. Number of cats in Europe 2010-2023

    • statista.com
    Updated Jun 27, 2025
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    Statista (2025). Number of cats in Europe 2010-2023 [Dataset]. https://www.statista.com/statistics/516041/cat-population-europe-europe/
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    Dataset updated
    Jun 27, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Europe
    Description

    This statistic presents the estimated number of cats owned by households in Europe in 2010, 2012, 2014, 2016, 2017, 2018, 2019, 2020, 2021, 2022, and 2023. The cat population in Europe was measured at approximately ****** million in 2023.

  20. Number of cats in Turkey 2012-2023

    • statista.com
    Updated Jul 9, 2025
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    Statista (2025). Number of cats in Turkey 2012-2023 [Dataset]. https://www.statista.com/statistics/516039/cat-population-europe-turkey/
    Explore at:
    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Türkiye
    Description

    The estimated number of cats owned by households in Turkey constantly increased in the observed years from 2012 to 2023. The cat population in Turkey peaked at over *** million in 2023.

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Hugging Face (2022). cats-image [Dataset]. https://huggingface.co/datasets/huggingface/cats-image
Organization logo

cats-image

huggingface/cats-image

Explore at:
397 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Apr 23, 2022
Dataset authored and provided by
Hugging Facehttps://huggingface.co/
Description

huggingface/cats-image dataset hosted on Hugging Face and contributed by the HF Datasets community

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