| --- |
| pretty_name: CountVideoBench |
| license: cc-by-4.0 |
| language: |
| - en |
| task_categories: |
| - video-text-to-text |
| - visual-question-answering |
| size_categories: |
| - 1K<n<10K |
| tags: |
| - video |
| - counting |
| - benchmark |
| - video-language-models |
| - synthetic |
| viewer: false |
| configs: |
| - config_name: single_class |
| default: true |
| drop_labels: true |
| data_files: |
| - split: test |
| path: "single_class/**" |
| --- |
| |
| # 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** · [Paper](https://arxiv.org/abs/2610.28356) · [Code](https://github.com/CountVideoBench/CountVideoBench) · [Project page](https://countvideobench.github.io) |
|
|
| ## 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`](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. |
| They are encoded as H.264 High 4:4:4 Predictive in RGB (`pix_fmt=gbrp`), which avoids chroma subsampling. Chromium-based browsers (Chrome, Edge, Brave) display these files with a pink tint when opened directly on the Hub; this is a browser display issue, not a problem with the files. They decode correctly in Firefox, VLC and any FFmpeg-based reader (decord, PyAV, torchvision, torchcodec, OpenCV). To browse the videos, open them in Firefox or download a single count folder (see [Usage](#usage)) and play the files locally. Example clips are also shown on the [project page](https://countvideobench.github.io). |
|
|
| ## Usage |
|
|
| Load the dataset in Python with the [`datasets`](https://huggingface.co/docs/datasets) library, or download the video files with the Hugging Face command-line tool [`hf`](https://huggingface.co/docs/huggingface_hub/guides/cli). The dataset is public: no account is needed. |
|
|
| ```python |
| # 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`](https://github.com/meta-pytorch/torchcodec) and FFmpeg. |
|
|
| To download the raw files instead: |
|
|
| ```bash |
| # 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: |
|
|
| ```bibtex |
| @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 |
|
|
