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

A large set of images of cats and dogs. There are 1738 corrupted images that are dropped.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('cats_vs_dogs', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/cats_vs_dogs-4.0.1.png" alt="Visualization" width="500px">

Small sample of the Kaggle Cats and Dogs dataset (https://www.kaggle.com/c/dogs-vs-cats/data).

Contains 1000 images for the train set (500 cats and 500 dogs), and 400 images for the test set (200 each).

Dogs And Cats

## Overview

Dogs And Cats is a dataset for object detection tasks - it contains Dogs And Cats annotations for 613 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Overview

This dataset contains a total of 1000 images, with an equal distribution of 500 images of dog and 500 images of cat. The images are standardized to a resolution of 512x512 pixels.

Details

• Total Images: 1000
  • Dog: 500 images
  • Cat: 500 images
• Image Resolution: 512x512 pixels
• File Format: .png
• Source: Images generated using Stable Diffusion 1.5

Usage

This dataset is ideal for tasks such as: - Binary classification - Image recognition and processing - Machine learning and deep learning model training

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).

This dataset is a delightful mix of cat images and various others, designed with education in mind for beginners. Split into user-friendly train and test sets, it serves as a perfect playground to dive into building a basic neural network classifier. Happy learning!

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).

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.

Cats

## Overview

Cats is a dataset for object detection tasks - it contains Jets annotations for 10,000 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).

Dogs Or Cats

## Overview

Dogs Or Cats is a dataset for classification tasks - it contains Dogs Or Cats annotations for 4,950 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).

This dataset contains a collection of cat images curated for testing and evaluating cat name prediction models, and should be used with the "Predicting Cat Names with Deep Learning" notebook. These images have been separated from the main training dataset to provide an unbiased test set for model validation. The dataset does not include labels, allowing users to test their models in a real-world scenario.

This dataset contains de-identified participant responses to a personality measures about their cat's personality (adapted Scottish wildcat personality measure), including information on the age and sex of the cat. The personality measure has 52 items that each contain a personality characteristic and participants were asked to rate the extent their cat demonstrated that characteristic on a seven-point scale. This dataset also indicates if the cat came from Australia or New Zealand.

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.

S Cats

## Overview

S Cats is a dataset for object detection tasks - it contains Dogs annotations for 754 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).

Dataset Description

This dataset was constructed to support the development and evaluation of deep learning models for classifying pet cats and stray cats based on facial images. It consists of two main subsets:

LostCat-PS Dataset (2,019 images)

• Stray (1,010 images): Outdoor cats without collars and without ear notches, judged as unowned by image providers.
• Pet (1,009 images): Owner-declared pet cats, including adopted former community cats.

LostCat-PSC Dataset (3,026 images)

• Stray (1,010 images): Definition identical to LostCat-PS.
• Pet (1,009 images): Definition identical to LostCat-PS.
• Community (1,007 images): Outdoor cats without collars, with ear notches indicating community cat status, as judged by image providers.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16005601%2F48659401aa8455cd4f7a7af098a3b592%2Fpetandstray.png?generation=1745736151401475&alt=media">

Preprocessing Information

The dataset provides raw (unprocessed) images, enabling users to apply custom preprocessing workflows. In our study, the following preprocessing pipeline was used:

• Background Removal: Using the Rembg library to remove background elements, reducing the influence of non-facial cues.
• Face Detection: Applying OpenCV’s Haar Cascade classifier to accurately detect cat faces.
• Face Alignment: Aligning faces based on detected landmarks to horizontally align the eyes and normalize face scale.

These steps were designed to enhance model robustness by ensuring that learning is focused on facial features rather than on backgrounds or artifacts.

Additional Information

The images were collected from diverse sources, including personal photography, public contributions, and online platforms such as Google Search, X (formerly Twitter), Instagram, YouTube, and Pinterest. This diversity ensures broad coverage across different breeds, locations, and environmental conditions.

Researchers can leverage this dataset for a range of tasks, including binary and multi-class cat classification, domain adaptation studies, and robustness evaluations. Beyond technical advancements, the dataset is intended to support practical applications such as stray animal rescue and lost pet identification.

This dataset contains a collection of images featuring cats and dogs, intended for use in image classification tasks. It's a valuable resource for training and evaluating machine learning models that can distinguish between these two popular pet species. The dataset is well-structured, making it suitable for both beginners and experienced data scientists looking to build and test image classification algorithms. Dataset contains more than 27500 training and testing images of dog and cat

This dataset is a modelled dataset, describing the mean number 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 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.

CATS-ISS_L2O_N-M7.2-V3-01_05kmPro is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Night 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.