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README.md

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+---
+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 (511 clips)
+- `E2-Episode`: Episodic segment (280 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 | 511 | ~3min | ~8.7 GB |
+| E2-Episode | 280 | ~2min | ~4.8 GB |
+| **Total** | **4,574** | - | **~80 GB** |
+
+## Contact
+
+For questions or issues, please open an issue in the dataset repository.

dataset_info.json

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+{
+  "dataset_name": "VCBench Clipped Videos",
+  "version": "1.0",
+  "total_clips": 4574,
+  "total_size_gb": 80,
+  "categories": {
+    "O1-Snap": {
+      "description": "Current-state snapshot counting",
+      "clips": 252,
+      "multi_query": true
+    },
+    "O1-Delta": {
+      "description": "Current-state delta counting",
+      "clips": 98,
+      "multi_query": false
+    },
+    "O2-Unique": {
+      "description": "Global unique identity tracking",
+      "clips": 1869,
+      "multi_query": true
+    },
+    "O2-Gain": {
+      "description": "Windowed gain counting",
+      "clips": 78,
+      "multi_query": false
+    },
+    "E1-Action": {
+      "description": "Instantaneous action counting",
+      "clips": 1281,
+      "multi_query": true
+    },
+    "E1-Transit": {
+      "description": "State transition counting",
+      "clips": 205,
+      "multi_query": true
+    },
+    "E2-Periodic": {
+      "description": "Periodic action counting",
+      "clips": 511,
+      "multi_query": true
+    },
+    "E2-Episode": {
+      "description": "Episodic segment counting",
+      "clips": 280,
+      "multi_query": true
+    }
+  },
+  "video_properties": {
+    "format": "MP4",
+    "codec": "H.264",
+    "encoding_method": "copy (lossless)",
+    "frame_rates": ["3fps", "24fps", "25fps", "30fps", "60fps"],
+    "duration_accuracy": "±0.1s"
+  },
+  "source_datasets": [
+    "YouTube",
+    "RoomTour3D",
+    "Ego4D",
+    "ScanNet",
+    "ScanNetPP",
+    "ARKitScenes",
+    "TOMATO",
+    "CODa",
+    "OmniWorld",
+    "Simulated"
+  ],
+  "creation_date": "2026-03-01",
+  "quality_validated": true
+}

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