| --- |
| 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. |
|
|
