This data contains 3 files consisting of the model weights of Inception, Xception and ResNet152V2. All the three models were trained on the Animal 10 classification dataset. Out of which, ResNet152V2 performed the best with 93% Training Accuracy and 92% Testing Accuracy with the lowest loss among all and converged to the solution the fastest. The other models are still not bad and they performed roughly close to it, around 91% accuracy in both training and testing part. That's why I have included both of them. If you can, check them out. The models predictions and the model itself is available in the Notebook associated with this data set.

Rapidata Animals-10

We took this existing Animals-10 dataset from kaggle and cleaned it using Rapidata's crowd, as detailed in this blog post. If you get value from this dataset and would like to see more in the future, please consider liking it.

  Dataset Details

10 classes: Butterfly, Cat, Chicken, Cow, Dog, Elephant, Horse, Sheep Spider, Squirrel 23554 Images In total, 124k labels were collected by human annotators, so each image is cross-validated on average by 5… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Animals-10.

dgrnd4/animals-10 dataset hosted on Hugging Face and contributed by the HF Datasets community

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

Rapidata Other Animals-10

This dataset contains the remaining images that were included in the original Animals-10 (kaggle) and which were not sorted into one of the existing 10 classes (Rapidata Animals-10). If you get value from this dataset and would like to see more in the future, please consider liking it.

  Dataset Details

33 classes 103 Images

Curated by: @canwiper Funded by: Rapidata License: gpl-2.0

  Dataset Sources

Blog post describing the setup… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Other-Animals-10.

1) Data Introduction • The Animals-10 Dataset is an image classification dataset composed of animal photos collected from Google Images. It includes images from 9 animal categories. 2) Data Utilization (1) Characteristics of the Animals-10 Dataset: • The dataset consists of real-world animal images taken under various backgrounds, angles, and lighting conditions, making it suitable for generalization experiments. • Some images intentionally include mislabeled samples to simulate realistic conditions and evaluate model robustness. (2) Applications of the Animals-10 Dataset: • Animal image classification model development: This dataset can be used to train deep learning-based classification models for building automated animal recognition systems useful for biologists and researchers.

mountassir/animals-10 dataset hosted on Hugging Face and contributed by the HF Datasets community

Thanks to iNaturalist, we're able to collect the images of 500 animals that we divide into two classes namely Endangered and Not Endangered.

This dataset contains 250 Endangered and 250 Not Endangered animals images, with each classes having 10 animals.

Fake Animals

This dataset is a collection of 10 types of animal images created using Stable Diffusion 3.5 Large.

  Dataset Details







  Dataset Description

All images have a resolution of 1024x1024 and are divided into training and test sets with 1000 and 500 images, respectively. The classes are as follows:

'0': cat '1': dog '2': elephant '3': fish '4': giraffe '5': horse '6': lion '7': penguin '8': rabbit '9': tiger

The data was generated according to the… See the full description on the dataset page: https://huggingface.co/datasets/Prgckwb/fake-animals.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12294787%2F2e9b3b5a8f236aab36655b4a0db4e311%2Foverview.jpg?generation=1718265309709943&alt=media" alt="drawing" style="width:700px;"/>

WildlifeReID-10k is a wildlife re-identification dataset with more than 140k images of 10k individual animals. It is a collection of 37 existing wildlife re-identification datasets with additional processing steps. WildlifeReID-10k contains animals as diverse as marine turtles, primates, birds, African herbivores, marine mammals and domestic animals. We provide a Jupyter notebook with introduction to the dataset, a way to evaluate developed algorithms and a baseline performance. WildlifeReID-10k has two primary uses:

Design an algorithm to classify individual animals in images. This is the much more complicated task (with 10k fine-grained classes) and the intended use of the dataset.

Design an algorithm to classify species of animals. This is a simpler task (with 20 coarse-grained classes) requiring fewer resources. It is intended for researchers or interested public who want to develop their first methods on an interesting dataset.

The file contains the data of tha animal activity registered during 6 cycles in a weaned piglet comercial farm (6-20kg body mass) using a passive infrared detector. Data were obtained in a 10min interval, following the method proposed in "https://doi.org/http://dx.doi.org/10.1016/j.biosystemseng.2017.06.014."Each cycle last for 40-42 days.The data were obtained with a PID (OPTEX RX-40QZ ) mounted at 2.8 m high over the entrance door in a weaned piglets room with 300 animals capacity. The room has a dimension of 12x6m.

Started out as a pumpkin detector to test training YOLOv5. Now suffering from extensive feature creep and probably ending up as a cat/dog/spider/pumpkin/randomobjects-detector. Or as a desaster.

The dataset does not fit https://docs.ultralytics.com/tutorials/training-tips-best-results/ well. There are no background images and the labeling is often only partial. Especially in the humans and pumpkin category where there are often lots of objects in one photo people apparently (and understandably) got bored and did not labe everything. And of course the images from the cat-category don't have the humans in it labeled since they come from a cat-identification model which ignored humans. It will need a lot of time to fixt that.

Dataset used: - Cat and Dog Data: Cat / Dog Tutorial NVIDIA Jetson https://github.com/dusty-nv/jetson-inference/blob/master/docs/pytorch-cat-dog.md © 2016-2019 NVIDIA according to bottom of linked page - Spider Data: Kaggle Animal 10 image set https://www.kaggle.com/datasets/alessiocorrado99/animals10 Animal pictures of 10 different categories taken from google images Kaggle project licensed GPL 2 - Pumpkin Data: Kaggle "Vegetable Images" https://www.researchgate.net/publication/352846889_DCNN-Based_Vegetable_Image_Classification_Using_Transfer_Learning_A_Comparative_Study https://www.kaggle.com/datasets/misrakahmed/vegetable-image-dataset Kaggle project licensed CC BY-SA 4.0 - Some pumpkin images manually copied from google image search - https://universe.roboflow.com/chess-project/chess-sample-rzbmc Provided by a Roboflow user License: CC BY 4.0 - https://universe.roboflow.com/steve-pamer-cvmbg/pumpkins-gfjw5 Provided by a Roboflow user License: CC BY 4.0 - https://universe.roboflow.com/nbduy/pumpkin-ryavl Provided by a Roboflow user License: CC BY 4.0 - https://universe.roboflow.com/homeworktest-wbx8v/cat_test-1x0bl/dataset/2 - https://universe.roboflow.com/220616nishikura/catdetector - https://universe.roboflow.com/atoany/cats-s4d4i/dataset/2 - https://universe.roboflow.com/personal-vruc2/agricultured-ioth22 - https://universe.roboflow.com/sreyoshiworkspace-radu9/pet_detection - https://universe.roboflow.com/artyom-hystt/my-dogs-lcpqe - license: Public Domain url: https://universe.roboflow.com/dolazy7-gmail-com-3vj05/sweetpumpkin/dataset/2 - https://universe.roboflow.com/tristram-dacayan/social-distancing-g4pbu - https://universe.roboflow.com/fyp-3edkl/social-distancing-2ygx5 License MIT - Spiders: https://universe.roboflow.com/lucas-lins-souza/animals-train-yruka

Currently I can't guarantee it's all correctly licenced. Checks are in progress. Inform me if you see one of your pictures and want it to be removed!

This dataset is well-suited for basic exploratory analysis, allowing users to identify patterns and generate initial insights. It provides a snapshot of various animal species, focusing on key biological traits and ecological context. While limited in size, it offers enough variety to support basic analysis in areas such as behaviour, habitat distribution, and conservation status.

Dataset Overview

The Animal Traits and Habitats dataset includes a curated selection of animals, capturing essential details like type, speed, weight, lifespan, and threat level. It also includes information on geographic distribution and reproductive behaviour. Though modest in scale, the dataset is suitable for exploratory studies and educational projects.

Key Details

Total Entries: 500 rows, each representing a distinct animal. Columns: 10 columns, including: - Animal Type – Classification such as mammal, bird, reptile, etc. - Animal Name – Common or scientific name. - Speed – Typical movement speed. - Weight – Average body weight. - Continent – General location by continent. - Country – Specific countries of habitat. - Lifespan – Average life expectancy. - Migration – Whether the species migrates. - Reproduction – Reproductive traits or methods. - Threat Level – Conservation status or risk level.

This dataset is well-suited for basic exploratory analysis, allowing users to identify patterns and generate initial insights.

Animal Center Outcomes from Oct, 1st 2013 to May 5th 2025. Outcomes represent the status of animals as they leave the Animal Center. All animals receive a unique Animal ID during intake. Annually over 90% of animals entering the center, are adopted, transferred to rescue or returned to their owners. The Outcomes data set reflects that Austin, TX. is the largest "No Kill" city in the country.

Understanding and analyzing animal behavior is increasingly essential to protect endangered animal species. However, the application of advanced computer vision techniques in this regard is minimal, which boils down to lacking large and diverse datasets for training deep models.

To break the deadlock, we present LoTE-Animal, a large-scale endangered animal dataset collected over 12 years, to foster the application of deep learning in rare species conservation. The collected data contains vast variations such as ecological seasons, weather conditions, periods, viewpoints, and habitat scenes. So far, we retrieved at least 500K videos and 1.2 million images. Specifically, we selected and annotated 11 endangered animals for behavior understanding, including 10K video sequences for the action recognition task, 28K images for object detection, instance segmentation, and pose estimation tasks. In addition, we gathered 7K web images of the same species as source domain data for the domain adaptation task.

We provide evaluation results of representative vision understanding approaches and cross-domain experiments. LoTE-Animal dataset would facilitate the community to research more advanced machine learning models and explore new tasks to aid endangered animal conservation. Our dataset will be released with the paper.

Global Total Number of 10% Top-Cited Scientific Publications in Food Animals Share by Country (Units (Publications)), 2023 Discover more data with ReportLinker!

Animal Center Intakes from Oct, 1st 2013 to May, 5th 2025. Intakes represent the status of animals as they arrive at the Animal Center. All animals receive a unique Animal ID during intake. Annually over 90% of animals entering the center, are adopted, transferred to rescue or returned to their owners.

DPI, Days Post Injury; SD, Standard Deviation; L, Liter; vs, versus. One way ANOVA was performed to statistically analyze the data. Several post hoc Tukey tests were used to statistically analyze the significant differences between groups.

AP-10K is the first large-scale benchmark for general animal pose estimation, to facilitate the research in animal pose estimation. AP-10K consists of 10,015 images collected and filtered from 23 animal families and 60 species following the taxonomic rank and high-quality keypoint annotations labeled and checked manually.

Comprehensive dataset of 10 Animals in Vietnam as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.