Pet Videos Dataset

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Project Overview:

Objective

The “Pet Videos Dataset” project aims to create a complete dataset for training machine learning models. Our goal is to provide a useful resource that helps develop AI models for recognizing and analyzing pet videos. This dataset has many uses, such as pet behavior analysis, veterinary diagnostics, and creating pet-related content.

Scope

Our project involves the careful collection of pet videos from various sources. We then annotate these videos with relevant information, which helps train AI models. As a result, these models can accurately recognize and understand pet behaviors, species, and activities.

Sources

User Contributions: Enthusiastic pet owners and animal lovers from around the world have generously shared their pet videos to enrich our dataset.
Publicly Available Pet Video Datasets: Moreover, we have accessed publicly available pet video datasets to supplement our collection with a wide variety of pet species, breeds, and scenarios.
Licensed Content: Additionally, we’ve collaborated with content creators, pet influencers, and pet-centric platforms to obtain licensed pet videos. This ensures a well-rounded and authentic representation of pet behaviors.

Data Collection Metrics

Total Pet Videos Collected: Over 25,000 pet videos
User Contributions: 15,000 videos
Public Datasets: 7,000 videos
Licensed Content: 3,000 videos

Annotation Process

Stages

Behavior Recognition: We have marked each video to show specific pet actions like playing, eating, grooming, and more.
Species and Breed Recognition: Our notes include the pet species (dogs, cats, birds, etc.) and, when possible, their breeds.
Contextual Information: The notes also capture details like whether the setting is indoors or outdoors, the time of day, and if there are other animals or humans present.

Annotation Metrics

Pet Videos with Behavior Annotations: 25,000 videos
Species and Breed Annotations: 20,000 annotations
Contextual Information Annotations: 15,000 annotations

Quality Assurance

Stages

Annotation Verification: We have set up a strict review process, which includes experts in pet behavior, to check and confirm the accuracy of our labels.
Data Quality Control: We have put in a lot of effort to remove low-quality or noisy videos from our dataset. This ensures that the data is clean and trustworthy.
Data Security and Privacy: We follow strict data security rules to keep pet-related information safe. Also, we make sure any personal data is anonymized, and we always get permission when needed.

QA Metrics

Annotation Validation Cases: Review of 10,000 video annotations by pet behavior experts to confirm accuracy.
Data Cleansing: Removal of 2,000 low-quality or noisy videos from the dataset to maintain high data integrity.
Verification Rate: 95% of the annotations undergo a verification process to ensure they accurately represent the depicted behaviors and species.

Conclusion

The “Pet Videos Dataset” is a testament to our commitment to offering high-quality data for machine learning applications. For example, with extensive annotations and a diverse collection of pet videos, this dataset empowers AI models to excel in recognizing and understanding pet behaviors, species, and contexts. Additionally, whether you’re working on pet-related applications or exploring the fascinating world of animal behavior analysis, our dataset is a valuable resource that opens up exciting possibilities.