Images were gathered from Google searches and downloaded using app 'download all images' . I highly recommend this app as it is very fast and returns a zip file with the images which you can then unzip to a specific directory. I have developed a custom set of tools to create datasets. The first tool used creates a dataset framework in a specified directory I call Datasets. It inputs the name of the new dataset and creates a directory with that name and within that directory creates 4 subdirectories train, test, valid and storage. The storage directory is where the unzipped downloaded images are placed. Downloaded images can be a crazy mix of ungodly file names and image formats. I wrote a python program called order_by_size. It operates on the downloaded images, within the storage directory, It removes files with extensions that are not jpg, png, or bmp and deletes files that are below a user specified image size. Then it renames the files sequentially using "zeros" padding and converts them to jpg format, and orders the files so that the first file is the largest image size, 2nd file is the next largest and so on. For the images in your dataset you want to start with images that are large. Later these images will be cropped to a region of interest and you want these cropped images to be large and have sufficient pixel count so that features can be extracted by your classification model. Now that the files are sequentially ordered and have jpg extensions I use another program called duplicate delete. This program uses file hashing to detect duplicate images and deletes any duplicates. This prevents having images in common between the train, test and validation images when the files are partitioned. Now when you do a Google search you will get a lot of what you want and also a lot of junk. I wrote another python program called review_images that sequentially shows each of the images in the storage directory and you can elect to delete or keep the image if it is the correct type of image you want. This then eliminates unwanted images from the storage directory. Then comes the hard part. If you want to build a high quality dataset you should crop your images so that the resulting image has a high ratio of pixels in the region of interest to the total number of pixels. For that I use paint shop pro version 9. If you examine the dataset images you will see that in most cases the image of the cat takes up at least 50% of the pixels in the image. After all that is done I use the order_by_size program again with different parameters which converts all the images to a specified size. For this dataset I used 224 X 224 X3 as the image size. Now we have a uniform ordered and properly pruned set of images for a specific class like tigers for example. I wrote another python program called make_class, it inputs the new class name (tiger for example) and creates a new class sub directory in the train, test and valid directories. Then it partitions the images in the storage directory into train images, test images and validation images and stores them in the class directory of the train, test and valid directories. Finally I wrote another python program that creates a dataset csv file. To make a high quality dataset takes a lot of work but the tools I have generated helps to reduce the work load.

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 Cats

## Overview

Dogs Cats is a dataset for object detection tasks - it contains Dogs And Cats 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 [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

🐱 Cat Image Dataset 🐾

Description: This dataset contains a collection of high-resolution images featuring adorable cats in various poses, settings, and moods. From playful kittens to majestic felines, these images capture the beauty and charm of our beloved furry companions.

Content: - The dataset comprises a curated selection of cat images sourced from diverse sources, ensuring a wide range of breeds, colors, and environments. - Each image is labeled with relevant metadata, including breed (if available), resolution, and any additional attributes.

Potential Uses: - Image Classification: Train machine learning models to accurately classify cat breeds or predict other attributes based on image content. - Image Generation: Explore generative models to create realistic cat images or generate new variations based on existing data. - Image Enhancement: Develop algorithms for image enhancement, denoising, or restoration to improve the quality of cat images.

Acknowledgments: We would like to express our gratitude to the contributors, photographers, and data sources that made this dataset possible. Their dedication to capturing and sharing these wonderful cat images enriches our understanding and appreciation of these beloved animals.

License: The dataset is provided under [insert license type or link if applicable], ensuring that it can be used, shared, and modified for both personal and commercial projects with proper attribution.

Explore and Contribute: - Dive into the world of cats and unleash your creativity by exploring this dataset! - We welcome contributions, enhancements, and annotations from the Kaggle community to further enrich and expand this dataset for future use.

Feel free to customize and expand upon this draft according to your specific dataset details and goals!

Cats

## Overview

Cats is a dataset for instance segmentation tasks - it contains Roads annotations for 828 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.

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

Cats

## Overview

Cats is a dataset for object detection tasks - it contains Cats annotations for 1,330 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.

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

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 is a common binary classification task in the field of computer vision and machine learning. It involves distinguishing between images of dogs and images of cats..

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.

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

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

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.

According to a national pet owners survey, there was a total of approximately 95.6 million cats living in households in the United States in 2017. In the same year, some 68 percent of all U.S. households owned at least one pet.

Increasing pet expenditure

Whilst the number of households owning cats, and pets in general, has remained relatively consistent over the last few years, pet industry expenditure has steadily grown. Consumers are expected to spend a record breaking 75.38 billion U.S. dollars on their pets in 2019. The majority of pet market revenue comes from food sales, followed by veterinary care costs.

Shopping location preferences

When it comes to shopping locations, most consumers still purchase their pet products in physical retail stores. However, the number of consumers buying pet products online is on the rise. Dry cat food was the number one pet product bought online by cat owners in the United States in 2018.

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. Attribution statement:

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

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.