| | --- |
| | license: mit |
| | task_categories: |
| | - video-classification |
| | - question-answering |
| | language: |
| | - en |
| | tags: |
| | - video-understanding |
| | - temporal-reasoning |
| | - counting |
| | - benchmark |
| | size_categories: |
| | - 1K<n<10K |
| | --- |
| | |
| | # VCBench: Clipped Videos Dataset |
| |
|
| | ## Dataset Description |
| |
|
| | This dataset contains **4,574 clipped video segments** from the VCBench (Video Counting Benchmark), designed for evaluating spatial-temporal state maintenance capabilities in video understanding models. |
| |
|
| | ### Dataset Summary |
| |
|
| | - **Total Videos**: 4,574 clips |
| | - **Total Size**: ~80 GB |
| | - **Video Format**: MP4 (H.264) |
| | - **Categories**: 8 subcategories across object counting and event counting tasks |
| |
|
| | ### Categories |
| |
|
| | **Object Counting (2,297 clips)**: |
| | - `O1-Snap`: Current-state snapshot (252 clips) |
| | - `O1-Delta`: Current-state delta (98 clips) |
| | - `O2-Unique`: Global unique counting (1,869 clips) |
| | - `O2-Gain`: Windowed gain counting (78 clips) |
| |
|
| | **Event Counting (2,277 clips)**: |
| | - `E1-Action`: Instantaneous action (1,281 clips) |
| | - `E1-Transit`: State transition (205 clips) |
| | - `E2-Periodic`: Periodic action (280 clips) |
| | - `E2-Episode`: Episodic segment (511 clips) |
| |
|
| | ## File Naming Convention |
| |
|
| | ### Multi-query clips |
| | Format: `{category}_{question_id}_{query_index}.mp4` |
| |
|
| | Example: `e1action_0000_00.mp4`, `e1action_0000_01.mp4` |
| |
|
| | ### Single-query clips |
| | Format: `{category}_{question_id}.mp4` |
| |
|
| | Example: `o1delta_0007.mp4`, `o2gain_0000.mp4` |
| |
|
| | ## Video Properties |
| |
|
| | - **Encoding**: H.264 (using `-c copy` for lossless clipping) |
| | - **Frame Rates**: Preserved from source (3fps, 24fps, 25fps, 30fps, 60fps) |
| | - **Duration Accuracy**: ±0.1s from annotation timestamps |
| | - **Quality**: Original quality maintained (no re-encoding) |
| |
|
| | ## Source Datasets |
| |
|
| | Videos are clipped from multiple source datasets: |
| | - YouTube walking tours and sports videos |
| | - RoomTour3D (indoor navigation) |
| | - Ego4D (first-person view) |
| | - ScanNet, ScanNetPP, ARKitScenes (3D indoor scenes) |
| | - TOMATO, CODa, OmniWorld (temporal reasoning) |
| | - Simulated physics videos |
| |
|
| | ## Usage |
| |
|
| | ### Loading with Python |
| |
|
| | ```python |
| | from huggingface_hub import hf_hub_download |
| | import cv2 |
| | |
| | # Download a specific video |
| | video_path = hf_hub_download( |
| | repo_id="YOUR_USERNAME/VCBench", |
| | filename="e1action_0000_00.mp4", |
| | repo_type="dataset" |
| | ) |
| | |
| | # Load with OpenCV |
| | cap = cv2.VideoCapture(video_path) |
| | ``` |
| |
|
| | ### Batch Download |
| |
|
| | ```bash |
| | # Install huggingface-cli |
| | pip install huggingface_hub |
| | |
| | # Download entire dataset |
| | huggingface-cli download YOUR_USERNAME/VCBench --repo-type dataset --local-dir ./vcbench_videos |
| | ``` |
| |
|
| | ## Annotations |
| |
|
| | For complete annotations including questions, query points, and ground truth answers, please refer to the original VCBench repository: |
| | - Object counting annotations: `object_count_data/*.json` |
| | - Event counting annotations: `event_counting_data/*.json` |
| |
|
| | Each annotation file contains: |
| | - `id`: Question identifier |
| | - `source_dataset`: Original video source |
| | - `video_path`: Original video filename |
| | - `question`: Counting question |
| | - `query_time` or `query_points`: Timestamp(s) for queries |
| | - `count`: Ground truth answer(s) |
| |
|
| | ## Quality Validation |
| |
|
| | All videos have been validated for: |
| | - ✓ Duration accuracy (100% within ±0.1s) |
| | - ✓ Frame rate preservation (original fps maintained) |
| | - ✓ No frame drops or speed changes |
| | - ✓ Lossless clipping (no re-encoding artifacts) |
| |
|
| | ## Citation |
| |
|
| | If you use this dataset, please cite the VCBench paper: |
| |
|
| | ```bibtex |
| | @article{vcbench2026, |
| | title={VCBench: A Streaming Counting Benchmark for Spatial-Temporal State Maintenance}, |
| | author={[Authors]}, |
| | journal={[Journal/Conference]}, |
| | year={2026} |
| | } |
| | ``` |
| |
|
| | ## License |
| |
|
| | MIT License - See LICENSE file for details. |
| |
|
| | ## Dataset Statistics |
| |
|
| | | Category | Clips | Avg Duration | Total Size | |
| | |----------|-------|--------------|------------| |
| | | O1-Snap | 252 | ~2min | ~4.3 GB | |
| | | O1-Delta | 98 | ~1min | ~1.7 GB | |
| | | O2-Unique | 1,869 | ~3min | ~32 GB | |
| | | O2-Gain | 78 | ~1min | ~1.3 GB | |
| | | E1-Action | 1,281 | ~4min | ~28 GB | |
| | | E1-Transit | 205 | ~2min | ~3.5 GB | |
| | | E2-Periodic | 280 | ~3min | ~8.7 GB | |
| | | E2-Episode | 511 | ~2min | ~4.8 GB | |
| | | **Total** | **4,574** | - | **~80 GB** | |
| |
|
| | ## Contact |
| |
|
| | For questions or issues, please open an issue in the dataset repository. |
| |
|
