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 driver analyzes the number of domesticated pets and companion animals owned in the US. Pets, defined in this driver as either cats or dogs, provide personal company or protection but are not considered working animals or livestock. The American Pet Products Association (APPA) conducts a biennial National Pet Owners Survey, and the data used in the survey regarding cat and dog ownership is collected and discussed here.

Charles95/cats-vs-dogs dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

Co-evolutionary relationships associated with biogeographical context mediate the response of native prey to introduced predators, but this effect has not yet been demonstrated for domestic cats. We investigated the main factors influencing the vulnerability of prey species to domestic cat (Felis catus) predation across Australia, Europe, and North America, where domestic cats are introduced. In addition to prey data from empirical records, we used machine-learning models to compensate for unobserved prey in the diet of cats. We found continent-specific patterns of predation: birds were more frequently depredated by cats in Europe and North America, while mammals were favoured in Australia. Bird prey traits were consistent across continents, but those of mammalian prey diverged, notably in Australia. Differences between prey and non-prey species included mass, distribution, and reproductive traits, except in Australian mammals where there was no evidence for a relationship between mass and the probability of being prey. Many Australian mammal prey also have a high extinction risk, emphasizing their vulnerability compared to European and North American counterparts. Our findings highlight the role of eco-evolutionary context in assessing predation impacts and also demonstrate the potential for machine learning to identify at-risk species, thereby aiding global conservation efforts to reduce the negative impacts of introduced predators.

Overview

• Introduction
• Object Classes
  • Animals
  • Bobcat
  • Cattle
  • Ocelot
  • Opossum

Introduction

The dataset focuses on detecting various animal species captured in their natural habitats using camera traps. The goal is to annotate these images for object detection tasks. The classes include:

• Animals: General class for unspecified animal detections.
• Bobcat: Medium-sized wild cat native to North America.
• Cattle: Domesticated bovines, typically found in herds.
• Ocelot: Small wild cat with a distinct pattern of dark rosettes on light fur.
• Opossum: Marsupial with a prehensile tail, found in the Americas.

Object Classes

Animals

Description

A broad category that includes any animal detected that does not specifically fall into the other defined classes.

Instructions

• Annotate any animal present that is visible but not identifiable as one of the specific classes like Bobcat, Cattle, Ocelot, or Opossum.
• If multiple animals are present and difficult to distinguish individually, encapsulate the entire group if classification is unclear.

Bobcat

Description

Bobcats have a muscular frame, tufted ears, and a distinctive spotted pattern on short, tawny fur. They possess a short tail with a black tip.

Instructions

• Draw the bounding box around the body, ensuring the head and tail are included within the box. Look for the characteristic ear tufts and spotted fur.
• Do not label if only part of the body is visible and its identification is uncertain.

Cattle

Description

Cattle are large domesticated animals, typically with a robust and hefty build. They have non-descript fur patterns and are commonly seen in grazing environments.

Instructions

• Include the entire visible body within the bounding box, highlighting the bulk and distinguishable head shape and horns, if visible.
• Avoid annotating if the cattle are severely occluded or blurry beyond recognition.

Ocelot

Description

Ocelots have a sleek body with a striking coat pattern of dark rosettes and stripes on a lighter background. They are comparable in size to domestic cats but have more elongated limbs.

Instructions

• Focus on the distinct rosette patterns and body shape when outlining the bounding box.
• Ensure that the whole animal, particularly the patterned body, is captured within the box.

Opossum

Description

Opossums are small to medium-sized marsupials with a pointed snout and a hairless, prehensile tail. They have a greyish body and a white face.

Instructions

• Surround the entire body, ears, and tail within the bounding box, paying close attention to the pointed snout and distinct tail.
• Avoid labeling if the quality of the image makes clear identification impossible or if the opossum is less than 20% visible.

The Digital Geomorphic-GIS Map of Cat Island (5-meter accuracy 2007 mapping), Mississippi is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (cati_geomorphology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (cati_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (cati_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (cati_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (cati_geomorphology_metadata.txt or cati_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:11,500 and United States National Map Accuracy Standards features are within (horizontally) 9.7 meters or 31.9 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The diversity and size of this collection is primarily creditable to the late Ralph Wetzel. The collection grew as a consequence of Dr. Wetzel`s NSF-supported program on the mammals of Paraguay. One particularly exciting and notable result of this project was the rediscovery of the Chacoan peccary (Catagonus wagneri), once thought to be extinct. Wetzel later extended his collections to several other South American countries. As a result, our collection includes many South American marsupials, canids, and rodents. We believe that this collection ranks among the top 5 in the world with respect to South American cats (many of the species included are now considered to be endangered or at risk), and among the top 10 in its coverage of South American mammals.

The second most important geographic emphasis of this collection is North America with extensive series of a wide diversity of North American mammal species. Of particular note are 200 bobcat skulls, 503 domesticated and feral pig skulls, 752 river otter skulls, and 1600 fisher skulls. Taxonomic coverage of the New England fauna is very good. The collection includes moderate representation of mammals from other regions of the world, most notably from Lebanon, Iraq, Turkistan, England, and Germany (reflecting the interests of previous students).

This data set contains 48 L1-calibrated scenes from the Airborne Visible-InfraRed Imaging Spectrometer (AVIRIS), provided by NASA. All scenes are 224 bands and cropped to a standardised 512x512 size, stored as raw 16-bit unsigned integers, in little endian byte order and in band-sequential (BSQ) order. This data was collected over a varied range of locations across North America between 2008 and 2017 and is a selection of the open access data provided by NASA's Jet Propulsion Laboratory (JPL) at https://aviris.jpl.nasa.gov/dataportal/. Specific dates and locations of each scene may be identified using the flight ID number in the scene name. These scenes compose a test set to evaluate compression algorithms for hyperspectral data.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Habitatges d'ús turístic de la ciutat de Barcelona

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

CaTS-Bench Dataset

A comprehensive benchmark for evaluating multi-modal models on time series understanding, captioning, and reasoning tasks across diverse domains.

  Quickstart





  Install

git clone

After downloading the dataset

tar -xzvf CaTSBench.tar.gz

  Run and evaluate

Run inference on a pre-trained model:

Text + Image evaluation (multimodal)

python -m source.inference.llama_infer

Other supported… See the full description on the dataset page: https://huggingface.co/datasets/neurips2025submission/CaTS-Bench.

osbm/cats-dogs dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset includes both VoxCeleb and VoxCeleb2

  Multipart Zips

Already joined zips for convenience but these specified files are NOT part of the original datasets vox2_mp4_1.zip - vox2_mp4_6.zip vox2_aac_1.zip - vox2_aac_2.zip

  Joining Zip

cat vox1_dev* > vox1_dev_wav.zip

cat vox2_dev_aac* > vox2_aac.zip

cat vox2_dev_mp4* > vox2_mp4.zip

  Citation Information

@article{Nagrani19, author = "Arsha Nagrani and Joon~Son Chung and Weidi Xie and Andrew… See the full description on the dataset page: https://huggingface.co/datasets/ProgramComputer/voxceleb.