An estimated ** million households in the United States owned at least one dog according to a 2024/25 pet owners survey, making them the most widely owned type of pet across the U.S. at this time. Cats and freshwater fish ranked in second and third places, with around ** million and ** million households owning such pets, respectively. Freshwater vs. salt water fish Freshwater fish spend most or all their lives in fresh water. Fresh water’s main difference to salt water is the level of salinity. Freshwater fish have a range of physiological adaptations to enable them to live in such conditions. As the statistic makes clear, Americans keep a large number of freshwater aquatic species at home as pets. American pet owners In 2023, around ** percent of all households in the United States owned a pet. This is a decrease from 2020, but still around a ** percent increase from 1988. It is no surprise that as more and more households own pets, pet industry expenditure has also witnessed steady growth. Expenditure reached over *** billion U.S. dollars in 2022, almost a sixfold increase from 1998. The majority of pet product sales are still made in brick-and-mortar stores, despite the rise and evolution of e-commerce in the United States.

Dogs and cats have become the most important and successful pets through long-term domestication. People keep them for various reasons, such as their functional roles or for physical or psychological support. However, why humans are so attached to dogs and cats remains unclear. A comprehensive understanding of the current state of human preferences for dogs and cats and the potential influential factors behind it is required. Here, we investigate this question using two independent online datasets and anonymous questionnaires in China. We find that current human preferences for dog and cat videos are relatively higher than for most other interests, with video plays ranking among the top three out of fifteen interests. We also find genetic variations, gender, age, and economic development levels notably influence human preferences for dogs and cats. Specifically, dog and cat ownership are significantly associated with parents’ pet ownership of dogs and cats (Spearman’s rank correlation c..., , , # Human preferences for dogs and cats in China: the current situation and influencing factors of watching online videos and pet ownership

https://doi.org/10.5061/dryad.qfttdz0rr

This dataset contains three CSV data files, each corresponding to one of the three parts described in the study.

Description of the data and file structure

**“1, bilibili.csv†**: contains data extracted from the Bilibili website. Each row in the dataset represents yearly data for each popular channel. Missing data are indicated with NA.

• ID:Â The serial number for each video, ranging from 1 to 167368.
• year: The year the video was published on the website, from 2009 to 2021.
• Videourl:Â The URL of the video.
• plays:Â The total number of plays for the video.
• likes: The total number of likes for the video.
• sort: The ranking of the video in terms of play count among all popular videos in its channel for that year.
• channelID: The I...

Context

This dataset was created when I practiced webscraping.

Content

The data is a compilation of information on dogs who were available for adoption on December 12, 2019 in the Hungarian Database of Homeless Pets. In total, there were 2,937 dogs in the database. It contains information on dogs' names, breed, color, age, sex, the date they were found, and some characteristics of their personalities.

Inspiration

I thought it would be interesting to have a dataset that looks at adoptable dogs' characteristics. It is not really well-suited for prediction, but could be a good practice dataset for data visualization and working with categorical data.

In this competition, you'll write an algorithm to classify whether images contain either a dog or a cat. This is easy for humans, dogs, and cats. Your computer will find it a bit more difficult.

https://www.ethosvet.com/wp-content/uploads/cat-dog-625x375.png" alt="">

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 (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords.

Asirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface:

Asirra is unique because of its partnership with Petfinder.com, the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research. Image recognition attacks

While random guessing is the easiest form of attack, various forms of image recognition can allow an attacker to make guesses that are better than random. There is enormous diversity in the photo database (a wide variety of backgrounds, angles, poses, lighting, etc.), making accurate automatic classification difficult. In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459. State of the art

The current literature suggests machine classifiers can score above 80% accuracy on this task [1]. Therfore, Asirra is no longer considered safe from attack. We have created this contest to benchmark the latest computer vision and deep learning approaches to this problem. Can you crack the CAPTCHA? Can you improve the state of the art? Can you create lasting peace between cats and dogs?

Submission Format

Your submission should have a header. For each image in the test set, predict a label for its id (1 = dog, 0 = cat):

id,label 1,0 2,0 3,0 etc...

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About This Dataset

The Roboflow Thermal Dogs and People dataset is a collection of 203 thermal infrared images captured at various distances from people and dogs in a park and near a home. Some images are deliberately unannotated as they do not contain a person or dog (see the Dataset Health Check for more). Images were captured both portrait and landscape. (Roboflow auto-orient assures the annotations align regardless of the image orientation.)

Thermal images were captured using the Seek Compact XR Extra Range Thermal Imaging Camera for iPhone. The selected color palette is Spectra.

Example

This is an example image and annotation from the dataset: https://i.imgur.com/h9vhrqB.png" alt="Man and Dog">

Usage

Thermal images have a wide array of applications: monitoring machine performance, seeing in low light conditions, and adding another dimension to standard RGB scenarios. Infrared imaging is useful in security, wildlife detection,and hunting / outdoors recreation.

This dataset serves as a way to experiment with infrared images in Roboflow. (Or, you could build your own night time pet finder!)

Collecting Custom Data

Roboflow is happy to improve your operations with infrared imaging and computer vision. Services range from data collection to building automated monitoring systems leveraging computer vision. Reach out for more.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. :fa-spacer: Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

Introduction

Animal welfare is important because there are so many animals around the world suffering from being used for entertainment, food, medicine, fashion, scientific advancement, and as exotic pets. Every animal deserves to have a good life where they enjoy the benefits of the Five Domains.

About Dataset

We aim to reduce total suffering, society’s ability to reduce this in other animals – which feel pain, too – also matters.

This is especially true when we look at the numbers: every year, humans slaughter more than 80 billion land-based animals for farming alone. Most of these animals are raised in factory farms, often in painful and inhumane conditions.

Estimates for fish are more uncertain, but when we include them, these numbers more than double.

These numbers are large – but this also means that there are large opportunities to alleviate animal suffering by reducing the number of animals we use for food, science, cosmetics, and other industries and improving the living conditions of those we continue to raise.

On this page, you can find all of our data, and writing on animal welfare.

File 1: The estimated number of animal lives that go toward each kilogram of animal product purchased for retail sale, including direct deaths only. For example, the pork numbers include only the deaths of pigs slaughtered for food.

File 2: The estimated number of animal lives that go toward each kilogram of animal product purchased for retail sale, including direct and indirect deaths. For example, the pork numbers include the deaths of pigs slaughtered for food (direct) but also those who die pre-slaughter and feed fish given to those pigs (indirect).

File 3: The estimated quantity of edible meat produced per animal, measured in kilograms.

File 4: Different location on time span = 2013 - 2020

File 5: Share of hens in cages Share of hens housed in a barn or aviary Share of non-organic, free-range hens Share of organic, free-range hens Share of laying hens in unknown housing

File 6: Number of eggs from hens in organic, free-range farms Number of eggs from hens in non-organic, free-range farms Number of eggs from hens in barns Number of eggs from hens in (enriched) cages

File 7: Estimated number of farmed decapod crustaceans Estimated number of farmed decapod crustaceans (upper bound) Estimated number of decapod crustaceans (lower bound)

File 8: Estimated number of farmed fish Estimated number of farmed fish (upper bound) Estimated number of farmed fish (lower bound)

File 9: Share of cage-free eggs Share of all eggs that are produced in cage-free housing systems. This includes barns, pasture and free-range (non-organic and organic) eggs.

Lets diving in dataset and create some excellent notebook for visualization and types of prediction. So, Good luck.

By Hannah Ritchie, Pablo Rosado and Max Roser (Our world in data)

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City Street: We collected a multi-view video dataset of a busy city street using 5 synchronized cameras. The videos are about 1 hour long with 2.7k (2704×1520) resolution at 30 fps. We select Cameras 1, 3 and 4 for the experiment (see Fig. 6 bottom). The cameras’ intrinsic and extrinsic parameters are estimated using the calibration algorithm from [52]. 500 multi-view images are uniformly sampled from the videos, and the first 300 are used for training and remaining 200 for testing. The ground-truth 2D and 3D annotations are obtained as follows. The head positions of the first camera-view are annotated manually, and then projected to other views and adjusted manually. Next, for the second camera view, new people (not seen in the first view), are also annotated and then projected to the other views. This process is repeated until all people in the scene are annotated and associated across all camera views. Our dataset has larger crowd numbers (70-150), compared with PETS (20-40) and DukeMTMC (10-30). Our new dataset also contains more crowd scale variations and occlusions due to vehicles and fixed structures.

Relação nominal dos bolsistas e tutor do Programa de Educação Tutorial da UFOB.

Dataset used in following paper: Faulkes Z. 2013. How much is that crayfish in the window? Online monitoring of Marmorkrebs, Procambarus fallax f. virginalis (Hagen, 1870) in the North American pet trade. Freshwater Crayfish 19(1): 39-44.

Percentage of households with different types of pets by divisional secretariat division.