Create README.md

---
license: openrail
pretty_name: "ITTO: Instruction-Tuned Tracking Objects Benchmark"
tags:
- video
- point-tracking
- object-tracking
- occlusion
- motion-complexity
- benchmark
- computer-vision
size_categories:
- 1K<n<10K
---

# ITTO: Instruction-Tuned Tracking Objects Benchmark

**ITTO** is a real-world video benchmark for evaluating point tracking models under challenging motion and occlusion conditions. It highlights failure modes common in real-world settings but absent in many prior benchmarks, such as long-range motion, object reappearances, and complex scene dynamics.

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## 📁 Contents

- High-quality point annotations on videos from **MOSE**, **L-VOS**, and **Ego4D**
- Split metadata and annotations
- Scripts to assist downloading Ego4D clips (requires license)

⚠️ **Ego4D videos are not included**. Only metadata and identifiers are provided.

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## 📊 Statistics

- 72 real-world videos
- 1,373 annotated point tracks
- 11,449 total video frames
- Avg. track duration: 221.6 frames
- Avg. objects per video: 10.9
- Occlusion rate: 58.1%
- Reappearances per track: 5.86

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## 🧪 Evaluation

Tracks are evaluated along key axes:
- Occlusion recovery and re-identification
- Motion complexity and long-term memory degradation
- Performance under dense scenes

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## 📜 License

This benchmark is released under **CC BY-NC-SA 4.0** due to MOSE.

- **MOSE**: CC BY-NC-SA 4.0
- **L-VOS**: BSD 3-Clause
- **Ego4D**: Metadata only – raw videos must be downloaded separately via [ego4d-data.org](https://ego4d-data.org)

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## 📦 Usage

```python
from datasets import load_dataset

ds = load_dataset("your-username/itto", split="validation")

README.md

@@ -0,0 +1,14 @@
+---
+license: bigcode-openrail-m
+pretty_name: "ITTO: Instruction-Tuned Tracking Objects Benchmark"
+tags:
+- video
+- point-tracking
+- object-tracking
+- occlusion
+- motion-complexity
+- benchmark
+- computer-vision
+size_categories:
+- 1K<n<10K
+---