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.

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.

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 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).

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

This dataset, contains a curated collection of images featuring four distinct big cat species: lions, tigers, leopards, and cheetahs. The images were sourced using the DuckDuckGo search engine and are organized into separate directories for each animal. This dataset is ideal for machine learning and computer vision projects focused on image classification and species recognition. With this dataset, you can train and validate your models to accurately differentiate between these majestic big cats.

Dogs Vs Cats

## Overview

Dogs Vs Cats is a dataset for object detection tasks - it contains Animals annotations for 1,100 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).

cat dataset used for AIHW5 diffusion model.

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).

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

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

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.

Photos of a few cats taken from different angles from its front, back, left, right, top side and from CCTV point of view.

This dataset is a modelled dataset, describing a lower 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 lower 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

CATS-ISS_L2O_D-M7.2-V3-01_05kmLay is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Day Mode 7.2 Version 3-01 5 km Layer 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.

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

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).

CATS-ISS_L2O_D-M7.2-V3-00_05kmPro is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Day Mode 7.2 Version 3-00 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.

Abstract

This dataset, composed of 440 sounds, contains meows emitted by cats in different contexts. Specifically, 21 cats belonging to 2 breeds (Maine Coon and European Shorthair) have been repeatedly exposed to three different stimuli that were expected to induce the emission of meows:

1. Brushing - Cats were brushed by their owners in their home environment for a maximum of 5 minutes;
2. Isolation in an unfamiliar environment - Cats were transferred by their owners into an unfamiliar environment (e.g., a room in a different apartment or an office). Distance was minimized and the usual transportation routine was adopted so as to avoid discomfort to animals. The journey lasted less than 30 minutes and cats were allowed 30 minutes with their owners to recover from transportation, before being isolated in the unfamiliar environment, where they stayed alone for maximum 5 minutes;
3. Waiting for food - The owner started the routine operations that preceded food delivery in the usual environment the cat was familiar with. Food was given at most 5 minutes after the beginning of the experiment.

The dataset has been produced and employed in the context of an interdepartmental project of the University of Milan (for further information, please refer to this doi). The content of the dataset has been described in detail in a scientific work currently under review; the reference will be provided as soon as the paper is published.

File naming conventions

Files containing meows are in the dataset.zip archive. They are PCM streams (.wav).

Naming conventions follow the pattern C_NNNNN_BB_SS_OOOOO_RXX, which has to be exploded as follows:

• C = emission context (values: B = brushing; F = waiting for food; I: isolation in an unfamiliar environment);
• NNNNN = cat’s unique ID;
• BB = breed (values: MC = Maine Coon; EU: European Shorthair);
• SS = sex (values: FI = female, intact; FN: female, neutered; MI: male, intact; MN: male, neutered);
• OOOOO = cat owner’s unique ID;
• R = recording session (values: 1, 2 or 3)
• XX = vocalization counter (values: 01..99)

Extra content

The extra.zip archive contains excluded recordings (sounds other than meows emitted by cats) and uncut sequences of close vocalizations.

Terms of use

The dataset is open access for scientific research and non-commercial purposes.

The authors require to acknowledge their work and, in case of scientific publication, to cite the most suitable reference among the following entries:

Ntalampiras, S., Ludovico, L.A., Presti, G., Prato Previde, E., Battini, M., Cannas, S., Palestrini, C., Mattiello, S.: Automatic Classification of Cat Vocalizations Emitted in Different Contexts. Animals, vol. 9(8), pp. 543.1–543.14. MDPI (2019).

ISSN: 2076-2615

Ludovico, L.A., Ntalampiras, S., Presti, G., Cannas, S., Battini, M., Mattiello, S.: CatMeows: A Publicly-Available Dataset of Cat Vocalizations. In: Li, X., Lokoč, J., Mezaris, V., Patras, I., Schoeffmann, K., Skopal, T., Vrochidis, S. (eds.) MultiMedia Modeling. 27th International Conference, MMM 2021, Prague, Czech Republic, June 22–24, 2021, Proceedings, Part II, LNCS, vol. 12573, pp. 230–243. Springer International Publishing, Cham (2021).

ISBN: 978-3-030-67834-0 (print), 978-3-030-67835-7 (online)

ISSN: 0302-9743 (print), 1611-3349 (online)