Upload README.md

README.md

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+---
+license: mit
+task_categories:
+- video-classification
+- zero-shot-classification
+tags:
+- motion-detection
+- temporal-vision
+- speckle-noise
+size_categories:
+- n<1K
+---
+
+# Motion-Text Classifier Dataset
+
+A binary classification dataset for distinguishing between pure motion and text-revealed-through-motion in speckle noise videos.
+
+## Dataset Description
+
+This dataset contains 1000 videos (5 seconds each) designed to test motion-based perception:
+
+- **Motion-only (500 videos)**: Pure background noise scrolling horizontally
+- **Text videos (500 videos)**: Single words revealed through opposing foreground/background motion
+
+All videos use identical noise parameters to ensure the **only** distinguishing feature is the presence of foreground content.
+
+## Dataset Structure
+
+```
+dataset/
+├── train/
+│   ├── motion_0000.mp4 to motion_0449.mp4 (450 videos)
+│   ├── text_0000.mp4 to text_0449.mp4 (450 videos)
+│   └── metadata.csv
+└── test/
+    ├── motion_0450.mp4 to motion_0499.mp4 (50 videos)
+    ├── text_0450.mp4 to text_0499.mp4 (50 videos)
+    └── metadata.csv
+```
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load dataset
+dataset = load_dataset("mukul54/motion-text-classifier")
+
+# Access samples
+train_sample = dataset['train'][0]
+print(train_sample['label'])  # 'motion_only' or 'text_fg_bg'
+print(train_sample['text'])   # word shown (None for motion_only)
+```
+
+## Video Parameters
+
+All videos share identical generation parameters:
+
+| Parameter | Value |
+|-----------|-------|
+| Resolution | 960×540 |
+| FPS | 60 |
+| Duration | 5.0 seconds |
+| Noise Density | 0.5 |
+| Speckle Size | 1 |
+| Speed | 2 |
+| Direction | Horizontal |
+
+## Labels
+
+- **motion_only**: Background noise only (no foreground object)
+- **text_fg_bg**: Text revealed through opposite motion of foreground/background using the same noise pattern
+
+## Key Features
+
+- **Pure motion detection**: Text is invisible in static frames
+- **Controlled experiment**: All parameters identical except foreground content
+- **Temporal encoding**: Information only available through motion analysis
+
+## Use Cases
+
+- Testing motion perception in vision models
+- Temporal feature extraction benchmarks
+- Zero-shot video understanding
+- Motion-based object detection
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@dataset{motion_text_classifier,
+  title={Motion-Text Classifier: Speckle Noise Motion Detection Dataset},
+  author={Mukul},
+  year={2025},
+  publisher={HuggingFace},
+  url={https://huggingface.co/datasets/mukul54/motion-text-classifier}
+}
+```
+
+## License
+
+MIT License