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---
license: mit
configs:
- config_name: default
data_files:
- split: test
path: data/test-*.parquet
---
# CameraBench optical flow dataset
A balanced VQA dataset for evaluating camera motion understanding in videos.
## 📊 Dataset Statistics
- **Total Questions**: 249
- **Unique Videos**: 70
- **Unique Questions**: 13
- **Yes Answers**: 89 (35.7%)
- **No Answers**: 160 (64.3%)
- **Balance Ratio**: 0.56
- **Total Size**: 258.40 MB (0.25 GB)
- **Average Video Size**: 3.69 MB
## 🎯 Task Categories
This dataset covers various camera motion tasks.
## 📝 Dataset Format
The dataset consists of MP4 video files with frames and optical flows stored in Parquet format.
Each record contains:
- `video_name`: Original video filename
- `video_path`: Relative path to video file (e.g., `videos/video.mp4`)
- `frames`: Sequence of extracted video frames
- `optical_flows`: Sequence of optical flow visualizations
- `question`: Binary question about camera motion
- `label`: Answer ("Yes" or "No")
<!-- SPLIT-SECTION:train:START -->
## Split: train
### Statistics
- **Total Questions**: 5816
- **Unique Videos**: 207
- **Unique Questions**: 518
- **Yes Answers**: 2908 (50.0%)
- **No Answers**: 2908 (50.0%)
- **Balance Ratio**: 1.0
- **Total Size**: 5073.39 MB (4.95 GB)
- **Average Video Size**: 24.51 MB
### Format: WebDataset
This split uses WebDataset format for efficient streaming:
- **Tar Shards**: 16 tar files
- **Path**: `webdataset/train/train-*.tar`
- **Structure**: Each tar contains frames, optical flows, and metadata in WebDataset format
- **Usage**: Load with `webdataset` library for streaming access
```python
import webdataset as wds
dataset = wds.WebDataset("path/to/train-*.tar").decode("rgb")
for sample in dataset:
video_name = sample["video_name"]
frames = [sample[f"frame_{i:04d}.png"] for i in range(sample["num_frames"])]
flows = [sample[f"flow_{i:04d}.png"] for i in range(sample["num_flows"])]
# ...process sample...
```
<!-- SPLIT-SECTION:train:END -->
<!-- SPLIT-SECTION:test:START -->
## Split: test
### Statistics
- **Total Questions**: 282
- **Unique Videos**: 72
- **Unique Questions**: 12
- **Yes Answers**: 113 (40.1%)
- **No Answers**: 169 (59.9%)
- **Balance Ratio**: 0.6686390532544378
- **Total Size**: 296.45 MB (0.29 GB)
- **Average Video Size**: 4.12 MB
### Format: Parquet
This split uses Parquet format with embedded images:
- **Path**: `data/test-*.parquet`
- **Structure**: Sharded parquet files with Image columns for frames and optical flows
- **Usage**: Load with `datasets` library for easy access in HuggingFace ecosystem
```python
from datasets import load_dataset
dataset = load_dataset("your-repo-id", split="test")
for sample in dataset:
frames = sample["frames"] # List of PIL Images
flows = sample["optical_flows"] # List of PIL Images
# ...process sample...
```
<!-- SPLIT-SECTION:test:END -->
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