About the Dataset:

The Cats and Dogs Classification dataset is a widely used benchmark dataset in the field of computer vision. It consists of thousands of images of cats and dogs, with each image labeled as either a cat or a dog. This dataset serves as a fundamental resource for training and evaluating machine learning models for image classification tasks. Researchers and practitioners leverage this dataset to develop and test algorithms that can accurately distinguish between images of cats and dogs, contributing to advancements in computer vision technology.

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">

huggingface/cats-image 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.
  • Contamination level of 0.038. This means about 3.8% of the observations (rows) are anomalous.
• 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.

[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

The dataset provider, Solenix, is an international company providing software e...

Dogs vs. Cats Image Classification

The "Cats vs. Dogs" dataset is a comprehensive collection of high-quality images specifically curated for binary image classification tasks, focusing on distinguishing between images of cats and dogs. This dataset is designed to serve as an ideal benchmark for evaluating deep learning and data science models in the domain of image classification.

Dataset Composition: The dataset comprises three main folders, meticulously organized to facilitate model training, validation, and evaluation:

1. Training Set: This folder contains a total of 20,000 images, equally split between 10,000 images of cats and 10,000 images of dogs. These images have been handpicked to cover a wide range of poses, backgrounds, and lighting conditions, ensuring a diverse and representative training sample.

2. Test Set: The test set mirrors the training set in size, comprising 12,461 images, with 6,219 images of dogs and 6,242 images of cats. This set remains completely independent and is intended to assess the generalization ability of trained models on unseen data.

3. Validation Set: Specifically crafted for fine-tuning and hyperparameter tuning, the validation set consists of 5,000 images. It includes 2,500 images of cats and 2,500 images of dogs, providing an unbiased evaluation of model performance during the development phase.

Image Specifications: All images in the dataset adhere to consistent standards to eliminate any bias related to image quality or resolution. The images are stored in popular image formats (e.g., JPEG, PNG) and have been resized to a uniform resolution, enabling seamless input to most deep learning frameworks.

Use Case and Applications: The Cats vs. Dogs dataset is tailored for binary image classification tasks in the domain of computer vision and offers a multitude of practical applications. This dataset can be employed for:

• Training and benchmarking deep learning models for binary image classification.
• Evaluating the effectiveness of various data augmentation techniques.
• Research in transfer learning and fine-tuning pre-trained models.
• Advancing the state-of-the-art in image classification algorithms.
• Classroom education and academic research in machine learning and data science.

Disclaimer: While every effort has been made to ensure the quality and accuracy of the dataset, the creators cannot guarantee absolute perfection or absence of errors. Users are encouraged to verify the dataset's suitability for their specific purposes and report any potential issues to contribute to the dataset's improvement and enrichment.

License: The "Cats vs. Dogs" dataset is made available under an open-source license, fostering collaboration and knowledge sharing within the scientific community. Users are encouraged to adhere to the license terms, which will be detailed in the dataset documentation.

I hope this dataset will facilitate cutting-edge research and innovation in the fascinating field of deep learning and data science, propelling us toward a future where AI-powered computer vision systems bring transformative benefits to society.

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.

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).

Cats And Dogs Classification

## Overview

Cats And Dogs Classification is a dataset for classification tasks - it contains Cats Dogs annotations for 990 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.

The dataset is used for testing the proposed thermal image super-resolution model.

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

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.

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

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).

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.

The Oxford IIIT Cats dataset has only 2.4k images in total, which is relatively small😭

Don't worry, this extended version has 10k😎

Containing 12 different cat breeds as same as the Oxford IIIT Cats dataset🤗: * Abyssinian * Bengal * Birman * Bombay * British * Shorthair * Egyptian Mau * Maine Coon * Persian * Ragdoll * Russian Blue * Siamese * Sphynx

🐱Original Oxford IIIT Cats dataset:https://www.kaggle.com/datasets/imbikramsaha/cat-breeds/

🐈Images used for extension come from these following sources:

https://www.kaggle.com/datasets/shawngano/gano-cat-breed-image-collection/data

https://www.kaggle.com/datasets/knucharat/pop-cats

https://www.kaggle.com/datasets/denispotapov/cat-breeds-dataset-cleared

😉This is the Version 3 of the CatBreedsRefined project:https://www.kaggle.com/datasets/doctrinek/catbreedsrefined-7k

Classification Dogs And Cats

## Overview

Classification Dogs And Cats is a dataset for classification tasks - it contains DAP annotations for 697 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).

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)

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).