The Animal10N Training Set consists of 40,000 images of animals from 10 different classes. The images are labeled with the animal's class.

Explore the wonders of the ocean with our Aquatic Animal Image Dataset! This collection features a variety of high-quality images showcasing different species of marine life. Perfect for researchers, students, and AI enthusiasts interested in marine biology or image classification. Dive in and discover the beauty beneath the surface! 🌊🐠 #AquaticLife #ImageClassification #MarineBiology

This dataset was originally created by Dane Sprsiter. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/dane-sprsiter/barnyard.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

1) Data Introduction • The Wildlife Animals Images Dataset is a computer vision dataset designed for image classification and generation tasks, containing various images of wild animals.

2) Data Utilization (1) Characteristics of the Wildlife Animals Images Dataset: • The dataset includes animals with visually similar features, such as species from the canine (Canidae) and feline (Felidae) families, making it suitable for training models to distinguish between animals that are often easily confused.

(2) Applications of the Wildlife Animals Images Dataset: • Wild animal classification model training: Useful for developing deep learning-based image classifiers capable of distinguishing between animal species with high visual similarity.

OPC Animals

## Overview

OPC Animals is a dataset for object detection tasks - it contains Animal annotations for 7,258 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).

Dataset Card for "big-animal-dataset"

Hi! I combined animals 10 dataset, the oxford pets dataset, stanford dogs dataset, and the cats vs dogs dataset for a large animal dataset. More Information needed

In early 2017, the Bloomington Animal Shelter migrated management software from AnimalShelterNet to Shelter Manager. We attempted to preserve as much information as possible from the old system.

The outcome fields in animal shelter are scattered in multiple fields not just one, for example Dead on arrival, Put to sleep, Movement Type and others are all considered as part of outcome.

About Dataset (Low Light Animal Image Dataset(90 classes))

Context

Animals (also called Metazoa) are multicellular, eukaryotic organisms in the biological kingdom Animalia. With few exceptions, animals consume organic material, breathe oxygen, are able to move, can reproduce sexually, and go through an ontogenetic stage in which their body consists of a hollow sphere of cells, the blastula, during embryonic development. Over 1.5 million living animal species have been described—of which around 1 million are insects—but it has been estimated there are over 7 million animal species in total. Animals range in length from 8.5 micrometers (0.00033 in) to 33.6 meters (110 ft). They have complex interactions with each other and their environments, forming intricate food webs. The scientific study of animals is known as zoology.

Most living animal species are in Bilateria, a clade whose members have a bilaterally symmetric body plan. The Bilateria include the protostomes—in which many groups of invertebrates are found, such as nematodes, arthropods, and mollusks—and the deuterostomes, containing both the echinoderms as well as the chordates, the latter containing the vertebrates. Life forms interpreted as early animals were present in the Ediacaran biota of the late Precambrian. Many modern animal phyla became clearly established in the fossil record as marine species during the Cambrian explosion, which began around 542 million years ago. 6,331 groups of genes common to all living animals have been identified; these may have arisen from a single common ancestor that lived 650 million years ago.

Source: Wikipedia

Content

In this Dataset, we have 5400 Animal Images in 90 different categories or classes. These images were converted to low light using img2lowlight

Acknowledgement

This dataset has been created from https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals?resource=download

Dataset

This dataset was created by Deep pratap Singh

Released under Apache 2.0

Contents

Animals

## Overview

Animals is a dataset for object detection tasks - it contains Elephant annotations for 1,805 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.

Farm Animals

## Overview

Farm Animals is a dataset for object detection tasks - it contains Sheep Cow Deer Wild Boar annotations for 700 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 [MIT license](https://creativecommons.org/licenses/MIT).

This dataset contains images used in the monograph titled Zastosowanie wybranych metod uczenia głębokiego w wizji komputerowej (Application of Selected Deep Learning Methods in Computer Vision) to build the Faster R-CNN model. The full collection consists of 600 image files showing 6 classes of objects: kot (cat), krowa (cow), pies (dog), koń (horse), człowiek (human), owca (sheep) (https://drive.google.com/file/d/1g3O1Qq2YqmCb8WSMDHaoFJPoHe6yujlQ/view?usp=drive_link). All images were scaled so that the smaller side is no shorter than 600 pixels and the larger side is no longer than 1000 pixels. The set was randomly divided into a training part (75% of the full set) and a test part (25% of the full set). As a result, the training part contains 450 files, and the test part - 150. Both parts are balanced - they contain a similar number of detected objects. The images are labeled with bounding boxes in the Pacal VOC format, according to which the description of each file is contained in an XML file with the same name. The dataset can be used to build models for object detection.

This dataset consists of 101 animals from a zoo. There are 16 variables with various traits to describe the animals. The 7 Class Types are: Mammal, Bird, Reptile, Fish, Amphibian, Bug and Invertebrate

The purpose for this dataset is to be able to predict the classification of the animals, based upon the variables. It is the perfect dataset for those who are new to learning Machine Learning.

zoo.csv

Attribute Information: (name of attribute and type of value domain)

1. animal_name: Unique for each instance
2. hair Boolean
3. feathers Boolean
4. eggs Boolean
5. milk Boolean
6. airborne Boolean
7. aquatic Boolean
8. predator Boolean
9. toothed Boolean
10. backbone Boolean
11. breathes Boolean
12. venomous Boolean
13. fins Boolean
14. legs Numeric (set of values: {0,2,4,5,6,8})
15. tail Boolean
16. domestic Boolean
17. catsize Boolean
18. class_type Numeric (integer values in range [1,7])

class.csv

This csv describes the dataset

1. Class_Number Numeric (integer values in range [1,7])
2. Number_Of_Animal_Species_In_Class Numeric
3. Class_Type character -- The actual word description of the class
4. Animal_Names character -- The list of the animals that fall in the category of the class

Acknowledgements

UCI Machine Learning: https://archive.ics.uci.edu/ml/datasets/Zoo

Source Information -- Creator: Richard Forsyth -- Donor: Richard S. Forsyth 8 Grosvenor Avenue Mapperley Park Nottingham NG3 5DX 0602-621676 -- Date: 5/15/1990

Inspiration

What are the best machine learning ensembles/methods for classifying these animals based upon the variables given?

Objects And Animals

## Overview

Objects And Animals is a dataset for object detection tasks - it contains Objects Animals annotations for 273 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).

Dataset

This dataset was created by sandeep johnR

Contents

Fr0styKn1ght/Animals dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset of Animals listed in IUCN Red List.

Animals included in the dataset as of now -> 1. African Elephant 2. Amur Leopard 3. Artic Fox 4. Chimpanzee 5. Orangutan

Dataset

This dataset was created by SeaSky2508

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Jad.201012

Contents