metadata
license: mit
task_categories:
- visual-question-answering
- multiple-choice
language:
- en
pretty_name: VideoColorBench
size_categories:
- n<1K
configs:
- config_name: films
data_files:
- split: test
path: films/test-*
- config_name: youtube
data_files:
- split: test
path: youtube/test-*
VideoColorBench
Can a model recognize a famous video from just its colors?
Each image is a barcode of one video. Every stripe is the average color of a few seconds, in order from start to end. The model picks which video it is out of 10 options, so guessing gets 10%.
| config | questions | what's in it |
|---|---|---|
films |
100 | feature films |
youtube |
100 | the most viewed YouTube videos, mostly music videos |
from datasets import load_dataset
ds = load_dataset("loganbolton/VideoColorBench", "films", split="test")
ds[0]["image"], ds[0]["choices"], ds[0]["answer"]
Fields
| field | meaning |
|---|---|
image |
the barcode, 1000x200 PNG |
choices |
the 10 options |
answer, answer_index, answer_title |
the right option as a letter (A to J), an index (0 to 9) and text |
title, artist, year, genre |
about the video |
average_color |
mean color of the whole video as hex |
colorfulness |
how saturated the video is, near 0 for black and white |
seconds |
runtime |
source_url |
where the video came from |
The wrong options are picked to be plausible. Same genre, close in year, similar colorfulness.
Running a model
The code is at https://github.com/LoganBolton/VideoColorBench. Any OpenRouter model runs with one command.
export OPENROUTER_API_KEY=sk-or-...
uv run run.py --model openai/gpt-6.1-sol
Only barcodes are shared here, no frames from the videos.