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The dataset provides access to videos, images, metadata, and/or links to videos obtained from YouTube and other publicly available sources. The dataset authors, maintainers, and affiliated organizations do not own the copyrights to the videos or other third-party content referenced or linked within the dataset.
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K9Bench 🐕
K9Bench is a video question-answering benchmark for evaluating fine-grained understanding of canine actions, and interactions in real-world YouTube videos.
Dataset Summary
Each sample pairs a YouTube video with a multiple-choice question requiring detailed visual understanding of dog behavior. Questions span five categories: action sequence, cause-effect analysis, context analysis, interaction analysis, and posture analysis.
Dataset Structure
| Field | Type | Description |
|---|---|---|
idx |
int | Unique sequential index |
video_url |
string | Full YouTube URL |
question_category |
string | One of 5 categories |
question |
string | The question about the video |
options |
dict | Five answer choices (A–E) |
correct_answer |
string | Ground-truth answer (A–E) |
Question Categories
- action sequence — ordering of behavioral events
- cause-effect analysis — causal relationships between actions
- context analysis — situational and environmental influences
- interaction analysis — social signals between animals or humans
- posture analysis — body language and physical cues
Splits
| Split | Rows |
|---|---|
| test | 4,744 |
Usage
from datasets import load_dataset
ds = load_dataset("ogmen/K9Bench")
sample = ds["test"][0]
print(sample["question"])
print(sample["options"])
print(sample["correct_answer"])
Citation
@dataset{k9bench2026,
title = {K9-Bench: Evaluating Multimodal LLMs on Canine-Centric Videos},
year = {2026},
url = {https://huggingface.co/datasets/ogmen/K9Bench}
}
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