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.

This dataset contains the 10 yearly statistics of pedigree dog litter registrations submitted to the UK Kennel Club. There is also supplementary information on each dog breed that may be used to provide insight on the increase and decline of various breeds' popularity.

This dataset offers opportunities for exploratory data analysis, data visualisation and simple NLP, as well as predictive capability.

Some thoughts for analysis: + What commonalities are found within breed groups? + Can we predict which dog breeds are likely to become vulnerable? + What trends can we see in the emerging popularity of certain breeds? + Is demand in line with certain characteristics?

The UK Kennel Club recognises 221 pedigree dog breeds; this small dataset is suitable for beginners and intermediate individuals. Additional variables may be added in the future for more advanced analysis.

Cat Dog Person

## Overview

Cat Dog Person is a dataset for object detection tasks - it contains Cat Dog Person annotations for 815 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).

This dataset contains statistics regarding the impounding of animals. The statistics cover three categories of animals; cats, dogs and livestock. This dataset contains statistics regarding the impounding of animals. The statistics cover three categories of animals; cats, dogs and livestock.

Dogs and Cats Online Data

This dataset is about book series. It has 1 row and is filtered where the books is The people with the dogs. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

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:

Analysis of ‘Dog Tax Statistics Moers 2018 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/6fa705c4-f510-406e-bf23-882c1884e40b on 12 January 2022.

--- Dataset description provided by original source is as follows ---

The data set shall include, in addition to the total number of dogs, the distribution of dogs among households with 1, 2 and 3 or more.

In addition, the number of taxable households, the total receipts and the number of dogs are indicated with a tax reduction or exemption.

--- Original source retains full ownership of the source dataset ---

This dataset is a modelled dataset, describing the predicted population of dogs per postcode district (e.g. YO41). This dataset gives the mean estimate for population for each district, and was 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 postcode district 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.

Data gathered under the Control of Dogs Act 1986 in order to enforce legislation. Current data being held in order to initiate legal proceedings. Historical data is held in order to generate requested reports and statistics. Historical data is also used for FOI requests, claims, queries etc.

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.

This dataset is a modelled dataset, describing the predicted population of dogs per postcode district (e.g. YO41). This dataset gives the upper estimate for population for each district, and was generated as part of the delivery of commissioned research. The data contained within this dataset are modelled figures, based on upper 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 postcode district 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

Since the entry into force of the Landeshundegesetz NRW, the competent local regulatory authorities are obliged to report the number of dangerous dogs, dogs of certain breeds and large dogs (§§ 3, 10 and 11 LHundG NRW) reported to the district governments each year as of 31.12., divided according to the dog breeds listed in the law (only 8 selected breeds are to be reported for the large dogs according to § 11 and all other large dogs). The number of bite incidents and other incidents recorded in the reporting year must also be reported, again broken down by race. In addition, it is differentiated whether the bite incidents have led to injuries to humans or animals. In addition, the number of official findings on the dangerousness of dogs in individual cases (Section 3(3) of the LHundG NRW) as well as the number of criminal proceedings and OWi proceedings initiated in each case must be reported on a racial basis. The district governments summarise the reports received from the municipal regulatory authorities in a table and send this table to the Ministry in the first quarter of the following year. This checks the figures received for plausibility, summarises the figures for the respective year for the whole country, calculates from the reported absolute figures relative frequencies (e.g. number of bite incidents of a dog breed in relation to the total population of this breed) and creates graphic representations and comparison tables to the statistics of previous years from this data. The file below contains the complete individual annual statistics from 2013 onwards.

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...

Summary data for dogs denied entry to the United States by year, January 1, 2018—December 31,2020.

Here are a few use cases for this project:

1. Pet Adoption Agencies: To streamline the process of pairing dogs with potential adopters based on captured images. For instance, a person's image could help the system suggest dogs that are comfortable around people with certain attributes like age or gender.

2. Training Assistance: Dog trainers or pet shops could use this model to create or augment training modules. By understanding the dog-human interaction through images, they could get insights into the behavior of different breeds and develop better training techniques.

3. Security Applications: This model could be integrated into security systems to differentiate between human and dog movement. The system can then alert homeowners only to human intruders, reducing false alarms triggered by pet movement.

4. Smart Home Automation: In smart homes, based on the identification of the individual (dog or human), the system could adjust the settings accordingly. For instance, if a dog is identified in a specific room, it could adjust the temperature or play certain calming sounds.

5. Animal Shelter Management: The model could help in managing shelters better by identifying dogs and humans, and monitoring their interaction frequency. It could provide data on which dogs are being ignored, ensuring all animals get equal attention.

Over the last century, dogs have been increasingly used to detect rare and elusive species or traces of them. The use of wildlife detection dogs (WDD) is particularly well established in North America, Europe and Oceania, and projects deploying them have increased worldwide. However, if they are to make a significant contribution to conservation and management, their strengths, abilities, and limitations should be fully identified. We reviewed the use of WDD with particular focus on the breeds used in different countries and for various targets, as well as their overall performance compared to other methods, by developing and analysing a database of 1220 publications, including 916 scientific ones, covering 2464 individual cases - most of them (1840) scientific. With the worldwide increase in the use of WDD, associated tasks have changed and become much more diverse. Since 1930, reports exist for 62 countries and 407 animal, 42 plant, 26 fungi and 6 bacteria species. Altogether, 108 FCI-classified and 20 non-FCI-classified breeds have worked as WDD. While certain breeds have been preferred on different continents and for specific tasks and targets, they were not generally better suited for detection tasks than others. Overall, WDD usually worked more effectively than other monitoring methods. For each species group, regardless of breed, detection dogs were better than other methods in 88.71% of all cases and only worse in 0.98%. It was only for arthropods that Pinshers and Schnauzers performed worse than other breeds. For mono- and dicotyledons, detection dogs did less often outperform other methods. Although every breed can be trained as a WDD, choosing the most suitable dog for the task and target may speed up training and increase the chance of success. Albeit selection of the most appropriate WDD is important, excellent training, knowledge about the target density and suitability, and a proper study design all appeared to have the highest impact on performance. Moreover, an appropriate area, habitat and weather are crucial for detection dog work. When these factors are taken into consideration, WDD can be an outstanding monitoring method.

Methods We systematically searched for any publication using the following search terms in Google Scholar and ISI Web of Knowledge: wildlife detect* dog, species detect* dog, scat detect* dog, [species] + detect* dog, [author] + detect* dog, [country] + detect* dog, conservation (detect*) dog, predator (detect*) dog, protected species (detect*) dog, den detect* dog, roost detect* dog, plant detect* dog, canine detection, and tracking dog. We traced any potentially relevant cited publication and only included those in our review that we could check ourselves. We also collected publications if we got to know them otherwise and reviewed existing literature lists and compilations (Grimm-Seyfarth et al. 2021, Appendix S1.1). We focused mainly on scientific literature, including scientific papers, dissertations, and project reports. However, wildlife detection dogs were frequently used for conservation or management purposes without a scientific research project behind them. For a more comprehensive overview of their deployment and performance, we included popular science or newspaper articles when no scientific publication about the project was found. In addition, we used social media platforms to obtain many articles from different countries (Grimm-Seyfarth et al. 2021, Appendix S1.1). In order to avoid multiple citations of the same study for which publications from different sources have been published, we compared each new entry with the entries in the database and preferably included scientific publications, followed by books, popular science and newspaper articles.

We compiled the data in a relational database (Microsoft Access 2013) consisting of five basic tables: literature, dog breeds, target species, target types and countries. We classified dog breeds into the ten FCI classification groups and breeds not listed as “not classified”. We assigned mixed breeds to a main or first-mentioned breed or to the category “Mix” when they could not be assigned to a specific breed. We classified target species according to their Latin and English names, genus, family, order, class, phylum and kingdom, adding subspecies names if provided. If the dog detected species groups without further specification (e.g., bat or bird carcasses, rodents, weed), we retained this group only. Taxonomic changes due to splitting of taxa into several species were only made if the allocation to the new species was obvious from the geographic information provided or had already been done by other authors. We divided potential target types into: living or dead individuals; nests, dens, clutches, coveys, roosts; scat, urine, saliva, glandular secretion; spores, eggs; larvae; hair, feathers, pellets, shed skin; and different combinations thereof. Lastly, we classified countries according to the (sub-) continent into North, Central and South America, Europe, Asia, Africa, and Oceania, assigning Russia and Turkey to “Eurasia”. Furthermore, we assigned Australia, New Zealand, and all oceanic islands (including subantarctic islands) to “Oceania” and made no differentiation to Zealandia.

In a main table, we then assigned each breed-target species-country association per reference as a single “case”. We marked pure-breed dogs and added a second breed for mixed breeds (if provided), as well as the number of dogs per breed and reference (if not mentioned directly, “1” for mentioning “dog” and “2” for mentioning “dogs”). We also added specifications to the country (e.g. Islands). If available, we extracted results of the wildlife detection dog performance compared to other monitoring methods. We classified the performance into four categories: dogs were (i) better; (ii) equal; or (iii) worse than other methods tested; or (iv) mixed results. The factor in comparison was study-specific and could include speed per area or transect, area size, sample size, quality, detectability, specificity, sensitivity, accuracy, or precision. We relied on those conservative measures since different monitoring methods can hardly be compared otherwise. The category “mixed results” was given when the dogs were better at some factors but worse at others, or when the performance depended upon season, year, site, or dog. Since we designed the database as a relational database, IDs among the five basic tables and the main table were linked together for quick searches and queries.

Osteoarthritis (OA) is very common cause of chronic pain in dogs. We currently assume that all dogs with OA suffer similarly from pain and show similar altered sensitivity to sensory stimuli such as heat and pressure. However, in people suffering from OA, different types of pain associated with different sensory sensitivities are recognized, and these distinct pain patterns are likely associated with different underlying changes in the sensory nervous system. Furthermore, these distinct pain patterns are likely to predict response to different analgesic drugs. We predict, given the similarity between the disease of OA in dogs and people, that we will be able to identify similar distinct pain patterns in dogs suffering from osteoarthritis. We will study pet dogs with OA, recruited through liaison with veterinary surgeons. We will use a simple, validated experimental paradigm to determine underlying pain mechanisms in individual dogs and subsequently map the individual pain pattern or pain phenotype to allow us to link pain mechanism with clinical pain expression. These data support the publication "Alfaxalone anaesthesia facilitates electrophysiological recordings of nociceptive withdrawal reflexes in dogs (Canis familiaris" [PLoS One]

This dataset is about books. It has 1 row and is filtered where the book is Of dogs and other people : the art of Roy De Forest. It features 7 columns including author, publication date, language, and book publisher.

Cat&dog&people

## Overview

Cat&dog&people is a dataset for object detection tasks - it contains Cats annotations for 612 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).