100+ datasets found
  1. Big Cats Images Dataset

    • kaggle.com
    Updated Aug 4, 2023
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    Cyborg (2023). Big Cats Images Dataset [Dataset]. https://www.kaggle.com/datasets/crownedhead06/big-cats-images-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 4, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Cyborg
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Description

    Humans have long struggled to discern between these majestic big cats species, and now we invite data scientists, researchers, and enthusiasts to unleash the power of artificial intelligence. Your mission is to craft and fine-tune models that transcend human perception, differentiating between diverse big cat species with unrivaled accuracy.

    Our meticulously curated dataset lays the foundation for this remarkable undertaking. With images meticulously sourced from various habitats, the dataset forms a comprehensive compendium of big cat diversity. As a participant, you'll harness this trove of data to create models that decipher the intricate features distinguishing lions, tigers, cheetahs, and more. Through your innovative approach and algorithmic prowess, the challenge aims to crown the model that can elegantly navigate the spectrum of big cat species.

  2. P

    CATS Dataset

    • paperswithcode.com
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    Wayne Treible; Philip Saponaro; Scott Sorensen; Abhishek Kolagunda; Michael O'Neal; Brian Phelan; Kelly Sherbondy; Chandra Kambhamettu, CATS Dataset [Dataset]. https://paperswithcode.com/dataset/cats
    Explore at:
    Authors
    Wayne Treible; Philip Saponaro; Scott Sorensen; Abhishek Kolagunda; Michael O'Neal; Brian Phelan; Kelly Sherbondy; Chandra Kambhamettu
    Description

    A dataset consisting of stereo thermal, stereo color, and cross-modality image pairs with high accuracy ground truth (< 2mm) generated from a LiDAR. The authors scanned 100 cluttered indoor and 80 outdoor scenes featuring challenging environments and conditions. CATS contains approximately 1400 images of pedestrians, vehicles, electronics, and other thermally interesting objects in different environmental conditions, including nighttime, daytime, and foggy scenes.

  3. P

    Cats and Dogs Dataset

    • paperswithcode.com
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    Cats and Dogs Dataset [Dataset]. https://paperswithcode.com/dataset/cats-vs-dogs
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    Description

    A large set of images of cats and dogs.

    Homepage: https://www.microsoft.com/en-us/download/details.aspx?id=54765

    Source code: tfds.image_classification.CatsVsDogs

    Versions:

    4.0.0 (default): New split API (https://tensorflow.org/datasets/splits) Download size: 786.68 MiB

    Source: https://www.tensorflow.org/datasets/catalog/cats_vs_dogs

  4. 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
    Explore at:
    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.

  5. 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
    Explore at:
    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.

  6. 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
    Explore at:
    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.

  7. Cat Images Classification Dataset

    • kaggle.com
    Updated Oct 19, 2023
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    Satwik Tanwar (2023). Cat Images Classification Dataset [Dataset]. https://www.kaggle.com/datasets/satwiktanwar/cat-images-classification-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 19, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Satwik Tanwar
    License

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

    Description

    The dataset contains two files in h5 format: 1. test_catvnoncat.h5: It contains 50 test examples of cat and non-cat images 2. train_catvnoncat.h5: It contains 209 train examples of cat and non-cat images

    The dataset contains images of size 64x64. The task is to classify an image as a cat (1) or not a cat (0). I am going to publish a series of notebooks for this dataset that would demonstrate neural networks from very basic level. Each notebook will build upon the previous one. Stay tuned to learn neural networks with the help of those notebooks!

    You can use the below code snippet to load and visualize the dataset. ```python import numpy as np import matplotlib.pyplot as plt import h5py import os

    for dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: print(os.path.join(dirname, filename))

    def load_data(): train_dataset = h5py.File('/kaggle/input/cat-images-classification-dataset/train_catvnoncat.h5', "r") train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels

    test_dataset = h5py.File('/kaggle/input/cat-images-classification-dataset/test_catvnoncat.h5', "r")
    test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
    test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels
    
    classes = np.array(test_dataset["list_classes"][:]) # the list of classes
    
    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
    
    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes
    

    load data

    train_x_orig, train_y, test_x_orig, test_y, classes = load_data()

    visualize an example image

    index = 10 plt.imshow(train_x_orig[index]) print ("y = " + str(train_y[0,index]) + ". It's a " + classes[train_y[0,index]].decode("utf-8") + " picture.") ```

  8. 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.

  9. R

    Different Cats Dataset

    • universe.roboflow.com
    zip
    Updated Aug 4, 2023
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    DatasetOD (2023). Different Cats Dataset [Dataset]. https://universe.roboflow.com/datasetod/different-cats
    Explore at:
    zipAvailable download formats
    Dataset updated
    Aug 4, 2023
    Dataset authored and provided by
    DatasetOD
    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

    Different Cats

    ## Overview
    
    Different Cats is a dataset for object detection tasks - it contains Cats annotations for 2,970 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).
    
  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. R

    Dogs And Cats Dataset Dataset

    • universe.roboflow.com
    zip
    Updated Feb 5, 2025
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    checking (2025). Dogs And Cats Dataset Dataset [Dataset]. https://universe.roboflow.com/checking-wtjkq/dogs-and-cats-dataset
    Explore at:
    zipAvailable download formats
    Dataset updated
    Feb 5, 2025
    Dataset authored and provided by
    checking
    License

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

    Variables measured
    Dogs And Cats Bounding Boxes
    Description

    Dogs And Cats Dataset

    ## Overview
    
    Dogs And Cats Dataset is a dataset for object detection tasks - it contains Dogs And Cats annotations for 515 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).
    
  12. Z

    Controlled Anomalies Time Series (CATS) Dataset

    • data.niaid.nih.gov
    Updated Jul 11, 2024
    + more versions
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    Patrick Fleith (2024). Controlled Anomalies Time Series (CATS) Dataset [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_7646896
    Explore at:
    Dataset updated
    Jul 11, 2024
    Dataset authored and provided by
    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.

  13. Dogs Vs Cats Dataset

    • kaggle.com
    Updated Oct 12, 2024
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    Sarim-Javed (2024). Dogs Vs Cats Dataset [Dataset]. https://www.kaggle.com/datasets/sarimr/dogs-vs-cats-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 12, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Sarim-Javed
    License

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

    Description

    Dataset

    This dataset was created by Sarim Javed

    Released under MIT

    Contents

  14. e

    Cats per square kilometre- upper 95th percentile

    • data.europa.eu
    • environment.data.gov.uk
    • +1more
    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

  15. 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
    Explore at:
    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).
    
  16. Number of cats in Turkey 2012-2023

    • statista.com
    Updated Jul 23, 2024
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    Statista (2024). Number of cats in Turkey 2012-2023 [Dataset]. https://www.statista.com/statistics/516039/cat-population-europe-turkey/
    Explore at:
    Dataset updated
    Jul 23, 2024
    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 4.7 million in 2023.

  17. a

    Cat Annotation Dataset Merged

    • academictorrents.com
    bittorrent
    Updated Jul 2, 2014
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    Weiwei Zhang and Jian Sun and Xiaoou Tang (2014). Cat Annotation Dataset Merged [Dataset]. https://academictorrents.com/details/c501571c29d16d7f41d159d699d0e7fb37092cbd
    Explore at:
    bittorrent(1980831996)Available download formats
    Dataset updated
    Jul 2, 2014
    Dataset authored and provided by
    Weiwei Zhang and Jian Sun and Xiaoou Tang
    License

    https://academictorrents.com/nolicensespecifiedhttps://academictorrents.com/nolicensespecified

    Description

    Cat Annotation Dataset The CAT dataset includes 10,000 cat images. For each image, we annotate the head of cat with nine points, two for eyes, one for mouth, and six for ears. The detail configuration of the annotation was shown in Figure 6 of the original paper: Weiwei Zhang, Jian Sun, and Xiaoou Tang, "Cat Head Detection - How to Effectively Exploit Shape and Texture Features", Proc. of European Conf. Computer Vision, vol. 4, pp.802-816, 2008. ### Format The annotation data are stored in a file with the name of the corresponding cat image plus ".cat", one annotation file for each cat image. For each annotation file, the annotation data are stored in the following sequence: 1. Number of points (always 9) 2. Left Eye 3. Right Eye 4. Mouth 5. Left Ear-1 6. Left Ear-2 7. Left Ear-3 8. Right Ear-1 9. Right Ear-2 10. Right Ear-3 ### Training, Validation, and Testing We randomly divide the data into three sets: 5,000 images for training, 2,000 images for valid

  18. h

    cats-vs-dogs-imbalanced

    • huggingface.co
    Updated Jul 4, 2024
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    Voxel51 (2024). cats-vs-dogs-imbalanced [Dataset]. https://huggingface.co/datasets/Voxel51/cats-vs-dogs-imbalanced
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 4, 2024
    Dataset authored and provided by
    Voxel51
    Description

    Dataset Card for cats-vs-dogs-imbalanced

    This is a FiftyOne dataset with 2551 samples.

      Installation
    

    If you haven't already, install FiftyOne: pip install -U fiftyone

      Usage
    

    import fiftyone as fo import fiftyone.utils.huggingface as fouh

    Load the dataset

    Note: other available arguments include 'max_samples', etc

    dataset = fouh.load_from_hub("Voxel51/cats-vs-dogs-imbalanced")

    Launch the App

    session = fo.launch_app(dataset)

      Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/cats-vs-dogs-imbalanced.
    
  19. h

    pookie3000_ascii-cats

    • huggingface.co
    Updated Jun 21, 2025
    + more versions
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    Olagunju Kazeem (2025). pookie3000_ascii-cats [Dataset]. https://huggingface.co/datasets/Holamick/pookie3000_ascii-cats
    Explore at:
    Dataset updated
    Jun 21, 2025
    Authors
    Olagunju Kazeem
    Description

    Holamick/pookie3000_ascii-cats dataset hosted on Hugging Face and contributed by the HF Datasets community

  20. p

    Licensed Dogs and Cats - Dataset - CKAN

    • ckan0.cf.opendata.inter.prod-toronto.ca
    Updated Dec 6, 2024
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    (2024). Licensed Dogs and Cats - Dataset - CKAN [Dataset]. https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/licensed-dogs-and-cats
    Explore at:
    Dataset updated
    Dec 6, 2024
    Description

    The reports include data on licensed dogs and cats residing in Toronto after year 2023, including primary breed, Forward Sortation Area (FSA), and year of licensing. For data before 2023, please visit: Licensed Dog and Cat Names: https://open.toronto.ca/dataset/licensed-dog-and-cat-names/ Licensed Dogs and Cats Reports: https://open.toronto.ca/dataset/licensed-dogs-and-cats-reports/

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Cyborg (2023). Big Cats Images Dataset [Dataset]. https://www.kaggle.com/datasets/crownedhead06/big-cats-images-dataset
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Big Cats Images Dataset

Large image classification dataset on top 10 breeds of Big Cats aka Felidae

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CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Aug 4, 2023
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Cyborg
License

Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
License information was derived automatically

Description

Humans have long struggled to discern between these majestic big cats species, and now we invite data scientists, researchers, and enthusiasts to unleash the power of artificial intelligence. Your mission is to craft and fine-tune models that transcend human perception, differentiating between diverse big cat species with unrivaled accuracy.

Our meticulously curated dataset lays the foundation for this remarkable undertaking. With images meticulously sourced from various habitats, the dataset forms a comprehensive compendium of big cat diversity. As a participant, you'll harness this trove of data to create models that decipher the intricate features distinguishing lions, tigers, cheetahs, and more. Through your innovative approach and algorithmic prowess, the challenge aims to crown the model that can elegantly navigate the spectrum of big cat species.

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