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# 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.
---
## 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)
```
---
# 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
---
# 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
---
# 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.
---
# 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.
---
# 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
---
# Contact
For questions related to this sample or the Physical AI Workbench:
https://voxel51.com