# Dataset Card for nvidia-physical-ai-sample

This dataset is a **small curated sample (100 items)** extracted from the full  
[NVIDIA PhysicalAI Autonomous Vehicles dataset](https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles).  
It is intended for **quick experimentation**, **tutorials**, and **FiftyOne integration demos** without requiring the multi-terabyte original dataset.

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## Installation

If you haven't already, install FiftyOne:

```bash
pip install -U fiftyone
```

---

## Usage

```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the sample dataset
dataset = load_from_hub("dgural/PhysicalAI-Autonomous-Vehicles-Sample")

# Launch the App
session = fo.launch_app(dataset)
```

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# Dataset Details

## Dataset Description

This dataset provides a **representative slice** of the NVIDIA PhysicalAI Autonomous Vehicles dataset, including:

- Camera
- A structure identical to the full dataset, suitable for:
  - Pipeline prototyping  
  - Instructional demos  
  - AV data exploration with FiftyOne  
  - Quick testing of loaders/adapters/exporters  

The full dataset is available at:  
**https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles**

### Curated by
Voxel51 (sample extraction), derived from NVIDIA’s original dataset.

### Language(s)
- en (metadata)

### License
Inherits licensing from the original NVIDIA dataset.  
See the main dataset page for license details.

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## Dataset Sources

- **Primary Dataset:** NVIDIA PhysicalAI Autonomous Vehicles  
- **Sample Extraction:** Voxel51 using FiftyOne + Physical AI Workbench pipelines  
- **Repository:** https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles  
- **Demo Code:** https://github.com/voxel51/fiftyone  

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# Uses

## Direct Use
Appropriate uses of this dataset include:

- Testing dataset import/export mechanisms  
- Unit tests for dataset auditing logic  
- Teaching users how to navigate AV sensor datasets  
- Lightweight experimentation  

## Out-of-Scope Use
This sample is **not** suitable for:

- Training ML models  
- Benchmarking performance  
- Statistical analysis  
- Scenario diversity evaluation  
- Research intended to generalize across AV driving conditions  

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# Dataset Structure

This sample preserves the same organizational layout as the full PhysicalAI dataset:

- Per-sample grouped data  

Each sample corresponds to a discrete AV sensor datapoint.

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# Dataset Creation

## Curation Rationale

The full PhysicalAI dataset is extremely large.  
This sample provides a lightweight, highly portable subset that can be used for:

- Rapid experimentation  
- Prototyping ingestion pipelines  
- Teaching and demos  
- Running on laptops or small instances  

## Source Data

The underlying data originates from NVIDIA’s PhysicalAI dataset.  
The sample was created by subselecting a limited number of frames and repacking them while preserving field structure.

### Source data produced by
NVIDIA Autonomous Vehicles & PhysicalAI teams.

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# Bias, Risks, and Limitations

Because this is a **non-representative sample**, it:

- Does *not* capture full scenario diversity  
- Should *not* be used for model training  
- Cannot support robust statistical evaluation  
- May omit critical driving edge cases  

It is designed solely for small-scale experimentation.

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# Citation

If you use this dataset or sample, cite the original:

**NVIDIA PhysicalAI Autonomous Vehicles Dataset**  
https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles

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# Contact

For questions related to this sample or the Physical AI Workbench:  
https://voxel51.com