CountVideoBench

A benchmark to evaluate prediction biases on the object counting task by video-language models.

Marc Chapus, Carlos Crispim-Junior, Véronique Eglin, Atilla Baskurt LIRIS, UMR 5205 — INSA Lyon, Université Lumière Lyon 2, CNRS, Université Claude Bernard Lyon 1

BMVC 2026 · Project page

Dataset summary

CountVideoBench contains 2,600 synthetic videos rendered with Blender, each showing a known number of identical objects. It is designed to isolate cardinality as the variable under study:

• Balanced by cardinality: 26 counts between 5 and 50, with exactly 100 videos per count. The counts are organised in clusters around six anchors (5, 10, 20, 30, 40, 50), each anchor with its neighbours at ±1 and ±2, so that accuracy can be compared across near-identical inputs: 5–12, 18–22, 28–32, 38–42, 48–50.
• Recognition kept simple: each video contains a single object class among 10 (2 shapes × 5 colours), with exactly 10 videos per class and per count.
• Static objects, moving camera: objects are static and the camera rotates in place. The white background carries a regular line pattern, and object surfaces a low-amplitude procedural noise, so that densely packed objects stay visually distinct.
• Exact ground truth: counts come from the generation process, with no human annotation.

The benchmark is far from saturated: no model evaluated in the paper exceeds 27.6% exact-match accuracy. On a 104-video subset, human participants reach 90.8%, against 26.9% for the best models on the same videos.

Example

Video: single_class/20_entities/vid_001001.mp4 (preview above, downscaled) — red cubes, 150 frames.

Question: How many red cubes are in this video?

Answer: 20

The question field of each video holds the exact prompt used in the paper. Using it unchanged keeps results comparable with those reported there.

Dataset structure

single_class/
├── metadata.csv
├── 05_entities/
│   ├── vid_000001.mp4
│   ├── ...
│   └── vid_000100.mp4
├── 06_entities/
├── ...
└── 50_entities/

Each folder holds the 100 videos of one count. metadata.csv has one row per video:

Field	Type	Description
file_name	string	Path to the video, relative to single_class/
video_id	string	Unique identifier (vid_000001 to vid_002600)
count	int	Ground-truth number of objects
shape	string	cube or sphere
color	string	red, green, blue, yellow or purple
question	string	Question asked to the model

Videos: MP4, 1920 × 1080, 30 fps, 5 seconds (150 frames). The full clips are provided so that users can choose their own frame sampling strategy.

Usage

Load the dataset in Python with the datasets library, or download the video files with the Hugging Face command-line tool hf. The dataset is public: no account is needed.

# pip install datasets
from datasets import load_dataset, Video

# Downloads the full dataset (139 GB) on first call
ds = load_dataset("countvideobench/CountVideoBench", "single_class", split="test")

# Keep file paths instead of decoded videos, to sample frames your own way
ds = ds.cast_column("video", Video(decode=False))

sample = ds[0]
print(sample["question"], sample["count"], sample["video"]["path"])

Without the cast_column line, accessing a row decodes its video, which requires torchcodec and FFmpeg.

To download the raw files instead:

# pip install -U huggingface_hub   (provides the hf command)

# All 2,600 videos
hf download countvideobench/CountVideoBench --repo-type dataset --local-dir CountVideoBench

# Or one count only, e.g. the 100 videos with 20 objects
hf download countvideobench/CountVideoBench --repo-type dataset --local-dir CountVideoBench \
  --include "single_class/metadata.csv" "single_class/20_entities/*"

Intended uses

• Evaluating object counting in video-language models.
• Diagnosing prediction biases per cardinality (preferred values, peaks and collapses in accuracy).
• Comparing models under identical, controlled conditions.

CountVideoBench is an evaluation benchmark: please do not use it as training data, so that reported results remain comparable.

Generation

Videos were rendered procedurally with Blender. All generation parameters (rendering, camera, objects, placement, physics, lighting) are documented in Appendix B of the paper.

Citation

If you use CountVideoBench, please cite:

@inproceedings{chapus2026countvideobench,
  author    = {Chapus, Marc and Crispim-Junior, Carlos and Eglin, V{\'e}ronique and Baskurt, Atilla},
  title     = {{CountVideoBench}: A Benchmark to Evaluate Prediction Biases on the Object Counting task by Video-Language Models},
  booktitle = {37th British Machine Vision Conference 2026, {BMVC} 2026, Lancaster, UK, November 23-26, 2026},
  publisher = {BMVA},
  year      = {2026}
}

Contact

Marc Chapus — marc.chapus@liris.cnrs.fr