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License information was derived automatically
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">
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.
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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.… See the full description on the dataset page: https://huggingface.co/datasets/Bingsu/Cat_and_Dog.
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">
64x64 resiezed rgb images to test your generative models
Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
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A dataset containing 5 common cat's breeds: 1. Bengal 2. Domestic Shorthair 3. Maine Coon 4. Ragdoll 5. Siamese
Dataset used for image classification and image detection.
Enjoy Using! 😄 Dog's Breed Dataset Chatbot AI Q&A Countries GDP
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 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.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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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..
http://i.imgur.com/LGI7wTt.png" alt="Imgur">
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.
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A small audio dataset generated from YouTube videos. The dataset has 566 cat sounds and 484 dog sounds. This was used as a contextual data source for research into contextual machine learning.
All efforts have been made to ensure this dataset was collected in line with copyright legislation regarding fair use. All samples were collected via YouTube and any derivative works of this provided dataset must reference YouTube and the author of this dataset.
This work was completed during a PhD programme at the University Of Greenwich.
In 2022, there were an estimated 14.9 million pet cats owned by households in France. That same year, the share of French households owning a cat was estimated at around 32 percent. France ranked among the two European countries where it was most common to have a cat: only Germany had a higher cat population. The number of cats in other countries, like in the United Kingdom or in Italy, was somewhat lower than that of Germany and France.
Pet cats in France
In 2022, the cat products segment accounted for 46 percent of the total sales revenue on the pet products market in France. The majority of French people cat-related expenses is dedicated to food.
Pet market in France On the pet products market, dry products made up the majority of the sales volume. For cats and dogs, a majority of sales are done in hyper and supermarkets, followed by garden centers.
The number of pet cats in Russia has been gradually expanding over the period under consideration. In 2022, there was a slight increase in the figures, with 23.15 million domestic cats recorded countrywide, compared to 22.95 million pets in the previous year.
Animal products’ market in Russia
With the growing population of domestic animals in the country, the market for animal products has been growing accordingly. Litter products accounted for the second-largest turnover in the animal products’ market after cat and dog food, at 24 billion Russian rubles in 2022. As for pet food sales growth, retail sales of cat food grew by 10.6 percent in monetary terms and 5.6 percent in physical terms between March 2021 and February 2022.
Pet-friendly Russia
Even though Russians were more prone to pet a cat rather than a dog, which was reflected by the lower number of domestic dogs in the country in 2019, a positive attitude towards domestic animals was largely widespread in the society. Namely, half of those who did not have any domestic animal in 2019, showed a disposition to adopt a pet from an animal shelter.
This survey depicts the prevalence of obese and overweight pet cats in the United States as of 2018. Over 33 percent of cats were reported to be obese and almost 26 percent to be overweight.
This is an example project demonstrating image classification via Convolutional Neural Networks. It imports an example dataset containing several thousand test and training images of cats and dogs, with which we can train and evaluate our model.
This project is heavily adapted from its initial publication at: https://gsurma.medium.com/image-classifier-cats-vs-dogs-with-convolutional-neural-networks-cnns-and-google-colabs-4e9af21ae7a8
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Dataset Card for Dataset Name
Dataset Description
Cite the dataset as: Patrick Fleith. (2023). Controlled Anomalies Time Series (CATS) Dataset (Version 2) [Data set]. Solenix Engineering GmbH. https://doi.org/10.5281/zenodo.8338435
Dataset Summary
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… See the full description on the dataset page: https://huggingface.co/datasets/patrickfleith/controlled-anomalies-time-series-dataset.
The Dutch are cat-loving people. In 2022, the total cat population in the country amounted to almost two million. By comparison: according to the most recent figures, there were about 1.5 million pet dogs in the Netherlands.
The most popular furry friend
Cats are the most frequently kept pets in the Netherlands. In 2022, one quarter of Dutch households owned a pet cat. This was slightly higher than the share of households owning a dog (21 percent), another beloved four-legged friend. Popular cats include the European Shorthair as well as various mixed breeds.
Cats in Europe
The number of pet cats amounted to over 127 million in Europe in 2022. Germany had a cat population of roughly 15.2 million that year, or over five times as large as the Netherlands. Other cat-loving nations included France and Italy (14.9 and 10.2 million cats respectively). The share of households owning a cat was highest in Romania though, where nearly half of all households owned at least one furry friend. In Poland and Hungary too, at least three out of ten households were cat owners. On the other hand, in Greece just 13 percent of households were ruled by one or more cats.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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]:
[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 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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Here are a few use cases for this project:
Animal identification app: Utilize the "cats-dogs-monkeys" model to create a mobile app that helps users identify and learn more about different types of animals, specifically cats, dogs, and monkeys, as well as their behaviors such as eating, walking, and sleeping.
Pet adoption platform: Integrate the model into an online pet adoption platform to filter and categorize pets based on species and behavior (such as cats, dogs, and monkeys) to help potential adopters more easily find their preferred pet.
Wildlife monitoring and research: Apply the model to analyze video footage or images from wildlife cameras or research studies to automatically categorize and document animal behaviors, helping researchers track and analyze animal populations and habits.
Pet care management system: Integrate the model into a smart pet care management tool, such as an automatic pet feeder or home monitoring camera, to identify pets and monitor their behavior, providing data to pet owners to better care for their pets and maintain their well-being.
Educational material creation: Utilize the model to generate educational content, such as interactive games, quizzes, or flashcards, to help children and students learn about animals, their behaviors, and other related information, making learning fun and engaging.
nateraw/auto-cats-and-dogs
Image Classification Dataset
Usage
from PIL import Image from datasets import load_dataset
def pil_loader(path: str): with open(path, 'rb') as f: im = Image.open(f) return im.convert('RGB')
def image_loader(example_batch): example_batch['image'] = [ pil_loader(f) for f in example_batch['file'] ] return example_batch
ds = load_dataset('nateraw/auto-cats-and-dogs') ds =… See the full description on the dataset page: https://huggingface.co/datasets/nateraw/auto-cats-and-dogs.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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">
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.