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

Animal Categories: Masters of Survival, Speed, and Strategy

This dataset categorizes animals into unique groups based on their survival traits, physical abilities, and behavioral strategies. From stealthy nocturnal hunters to fast-paced predators and intelligent problem-solvers, this collection highlights the diversity and adaptability of the animal kingdom. Each category is carefully curated to showcase the unique characteristics that make these animals stand out in their respective environments.

Dataset Structure: In labels.csv file

• image_name: The image_name column contains the file paths or names of the images. The names are structured in a way that indicates the type of animal or object in the image (e.g., beetle/687486f1cb.jpg, parrot/5affc48d37.jpg).
• category: The category column contains the label or category associated with each image. These categories describe the type of animal or object in the image, such as Tiny Survivors, Survival Geniuses, Apex Predators, etc.

Categories Included:

• Stealth & Shadows: Masters of camouflage and nocturnal survival (e.g., bat, leopard, owl).
• Speed Demons: Fastest animals on land, air, or water (e.g., cheetah, falcon, dolphin).
• Tough Defenders: Hard-shelled or armored animals (e.g., turtle, armadillo, hedgehog).
• Apex Predators: Top of the food chain (e.g., lion, shark, tiger).
• Survival Geniuses: Highly intelligent and skilled problem-solvers (e.g., chimpanzee, octopus, crow).
• Flight Masters: Birds and insects that dominate the skies (e.g., eagle, hummingbird, butterfly).
• Underwater Specialists: Ocean-based creatures (e.g., whale, jellyfish, seahorse).
• Cold-Climate Survivors: Adapted to harsh winters (e.g., penguin, polar bear, arctic fox).
• Pack Hunters & Social Strategists: Animals that work in groups to hunt or survive (e.g., wolf, lion, meerkat).
• Tiny Survivors: Small but resilient creatures (e.g., rat, cockroach, ladybugs).
• Other: Miscellaneous animals that don’t fit into the above categories.

Acknowledgments: This dataset was inspired by the incredible diversity of the animal kingdom. Special thanks to the dataset Animal Image Dataset (90 Different Animals) by Sourav Banerjee for providing a rich collection of animal images that can complement this categorical dataset. Combining these datasets could enable exciting projects, such as image classification or trait-based animal recognition.

Wild Animal

## Overview

Wild Animal is a dataset for object detection tasks - it contains Animal annotations for 503 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).

A structured animal image classification dataset containing 1,763 labeled images of cats, dogs, and horses. Designed for training, validation, and testing computer vision and deep learning models. Suitable for educational and research use cases.

Injured Animal Detector

## Overview

Injured Animal Detector is a dataset for object detection tasks - it contains Injured Animals annotations for 149 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Dataset

This dataset was created by sandeep johnR

Contents

Lite Animal-Dataset for image classification. 5 different animals includes:

Bear Cat Dog Elephant Goat

10 training photos for each class and 2 validation photos. This dataset can be use for image classification.

This dataset provides animal pose annotations on five categories are provided: dog, cat, cow, horse, sheep, with in total 6,000+ instances in 4,000+ images. Besides, the dataset also contains bounding box annotations for other 7 animal categories. Find details in the paper.

We annotate in total 20 keypoints: Two eyes, Throat, Nose, Withers, Two Earbases, Tailbase, Four Elbows, Four Knees, Four Paws. We select some samples from this dataset. The first figure represents keypoint-labeled animal instances from five animal categories. The second figure contains some animal images with only bounding box labeled from seven different categories: otter, bobcat, rhino, hippo, chimpanzee, bear and antelope.

This dataset features over 5,500,000 high-quality images of animals sourced from photographers around the globe. Created to support AI and machine learning applications, it offers a richly diverse and precisely annotated collection of wildlife, domestic, and exotic animal imagery.

Key Features: 1. Comprehensive Metadata: the dataset includes full EXIF data such as aperture, ISO, shutter speed, and focal length. Each image is pre-annotated with species information, behavior tags, and scene metadata, making it ideal for image classification, detection, and animal behavior modeling. Popularity metrics based on platform engagement are also included.

1. Unique Sourcing Capabilities: the images are gathered through a proprietary gamified platform that hosts competitions on animal photography. This approach ensures a stream of fresh, high-quality content. On-demand custom datasets can be delivered within 72 hours for specific species, habitats, or behavioral contexts.

2. Global Diversity: photographers from over 100 countries contribute to the dataset, capturing animals in a variety of ecosystems—forests, savannas, oceans, mountains, farms, and homes. It includes pets, wildlife, livestock, birds, marine life, and insects across a wide spectrum of climates and regions.

3. High-Quality Imagery: the dataset spans from standard to ultra-high-resolution images, suitable for close-up analysis of physical features or environmental interactions. A balance of candid, professional, and artistic photography styles ensures training value for real-world and creative AI tasks.

4. Popularity Scores: each image carries a popularity score from its performance in GuruShots competitions. This can be used to train AI models on visual appeal, species preference, or public interest trends.

5. AI-Ready Design: optimized for use in training models in species classification, object detection, wildlife monitoring, animal facial recognition, and habitat analysis. It integrates seamlessly with major ML frameworks and annotation tools.

6. Licensing & Compliance: all data complies with global data and wildlife imagery licensing regulations. Licenses are clear and flexible for commercial, nonprofit, and academic use.

Use Cases: 1. Training AI for wildlife identification and biodiversity monitoring. 2. Powering pet recognition, breed classification, and animal health AI tools. 3. Supporting AR/VR education tools and natural history simulations. 4. Enhancing environmental conservation and ecological research models.

This dataset offers a rich, high-quality resource for training AI and ML systems in zoology, conservation, agriculture, and consumer tech. Custom dataset requests are welcomed. Contact us to learn more!

Animal OD

## Overview

Animal OD is a dataset for object detection tasks - it contains Animals annotations for 1,150 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 a Dataset on the four main animals that act as major tourist attractions. Lion, Elephant, Cheetah and Rhinoceros. I was inspired to create this dataset because I was going to do a project on Image Detection on the four animals but it was so hard to find them and I didn't want people to face the same problem, so I created it. I used three datasets I found on kaggle to extract what I wanted to create this. Those three datasets are: 1. African Wildlife 2. Animals Detection Images 3. Wild Animals Images

I want to thank the authors of these datasets for the big help. I wouldn't have done it without their hard work and contributions

IF you liked it please UPVOTE

Is There An Animal

## Overview

Is There An Animal is a dataset for classification tasks - it contains Animals annotations for 430 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 provides comprehensive information about animals from Animalia.bio, covering a wide range of species, habitats, behaviors, and conservation statuses. It aims to support research, education, and data exploration related to wildlife and biodiversity.

Potential Uses: - Education: Develop learning materials or visualizations for wildlife awareness. - Research: Analyze species diversity, geographical distribution, or conservation trends. - Data Science Projects: Apply machine learning for habitat prediction, conservation priority ranking, or behavioral clustering.

Incidents responded to by the Baton Rouge Animal Control and Rescue Center (ACRC). ACRC is responsible for carrying out duties related to animal-related situations, including: administering the anti-rabies vaccination, licensing, and tag program; investigating animal cruelty incidents; investigating dog fighting; resolving dangerous animal situations; rescuing injured animals; investigating abandoned animal cases; investigating occult, animal sacrifice, and bestiality cases; resolving stray animal situations; enforcing the leash law and owned animal problems; assisting law enforcement with narcotics, evictions, and DWI cases; enforcing barking dog cases; inspecting dog yards/pens; chaining or tethering compliance; assisting animal welfare groups with feral interventions; and conducting educational programs. As many of the incidents included within this data set involve active cases that are currently under investigation and computerized system limitations do not allow for automated screening of open/closed cases, the identity of animal owners is redacted to protect the privacy of the animal owner. Members of the public interested in the identity of a specific incident may contact ACRC directly to inquire about the incident and, if it is closed, ACRC will release a copy of the file to the person requesting it. However, location data regarding where the incident was reported or occurred is included within this data set, which may or may not be the same location as the animal owner's home or property. In addition, to protect the identity of the complainant (person filing the complaint or alerting ACRC to a potential incident), only the complainant's street name is included as part of this data set. Finally, while all incidents are updated on a daily basis, incidents involving animal cruelty are updated based on a rolling 30-day delay to allow for ACRC to investigate the incident and make a determination as to the validity of the cruelty complaint.

Juli-Kath/animal-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Vu Quoc Viet

Contents

Since 2009 PSB has been collecting satellite tag telemetry data from sea turtles and other protected species.

The animal model market is estimated to be valued at USD 2.0 billion in 2025. It is projected to reach USD 3.6 billion by 2035, registering a compound annual growth rate (CAGR) of 6.0% over the forecast period.

Metric	Value
Estimated Size (2025E)	USD 2.0 billion
Projected Value (2035F)	USD 3.6 billion
CAGR (2025 to 2035)	6.0%

Animal Model Market Analysis by Top Countries

Country	CAGR (2025 to 2035)
USA	7.5%
Brazil	7.0%
Japan	6.5%
Germany	6.4%
France	6.3%
China	6.0%
UK	5.9%

J Animal

## Overview

J Animal is a dataset for object detection tasks - it contains Tanuli Serow Fox annotations for 28,281 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).

Just to give a rough estimate, it can be assumed that the market will have a value of USD 44.4 Billion in 2025, a number that could go up to USD 65.2 Billion in 2035, indicating an annual growth rate of 3.9% during the forecast period.

Metric	Value
Market Size (2025E)	USD 44.4 Billion
Market Value (2035F)	USD 65.2 Billion
CAGR (2025 to 2035)	3.9%

Country Wise Analysis

Country	CAGR (2025 to 2035)
USA	4.2%

Country	CAGR (2025 to 2035)
UK	3.8%

Country	CAGR (2025 to 2035)
European Union (EU)	3.9%

Country	CAGR (2025 to 2035)
Japan	4.1%

Country	CAGR (2025 to 2035)
South Korea	4.2%

Competitive Outlook

Company Name	Estimated Market Share (%)
Zoetis Inc.	15 to 20%
Boehringer Ingelheim Animal Health	12-16%
Merck Animal Health (MSD Animal Health)	10-14%
Elanco Animal Health Incorporated	8-12%
Ceva Santé Animale	5-9%
Other Companies (combined)	40-50%