Datasets:

dgural
/

PhysicalAI-Autonomous-Vehicles-Sample

@@ -1,71 +1,11 @@
----
-annotations_creators: []
-language: en
-size_categories:
-- n<1K
-task_categories: []
-task_ids: []
-pretty_name: nvidia-physical-ai
-tags:
-- fiftyone
-- group
-dataset_summary: '
-  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 100 samples.
-  ## 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 dataset
-  # Note: other available arguments include ''max_samples'', etc
-  dataset = load_from_hub("dgural/my-quickstart-dataset")
-  # Launch the App
-  session = fo.launch_app(dataset)
-  ```
-  '
 ---
-# Dataset Card for nvidia-physical-ai
-<!-- Provide a quick summary of the dataset. -->
-This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 100 samples.
 ## Installation
 If you haven't already, install FiftyOne:
@@ -74,149 +14,136 @@ If you haven't already, install FiftyOne:
 pip install -U fiftyone
 ```
 ## Usage
 ```python
 import fiftyone as fo
 from fiftyone.utils.huggingface import load_from_hub
-# Load the dataset
-# Note: other available arguments include 'max_samples', etc
-dataset = load_from_hub("dgural/my-quickstart-dataset")
 # Launch the App
 session = fo.launch_app(dataset)
 ```
-## Dataset Details
-### Dataset Description
-<!-- Provide a longer summary of what this dataset is. -->
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** [More Information Needed]
-### Dataset Sources [optional]
-<!-- Provide the basic links for the dataset. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the dataset is intended to be used. -->
-### Direct Use
-<!-- This section describes suitable use cases for the dataset. -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-[More Information Needed]
-## Dataset Structure
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-[More Information Needed]
-## Dataset Creation
-### Curation Rationale
-<!-- Motivation for the creation of this dataset. -->
-[More Information Needed]
-### Source Data
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-#### Data Collection and Processing
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-[More Information Needed]
-#### Who are the source data producers?
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-[More Information Needed]
-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-#### Annotation process
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-[More Information Needed]
-#### Who are the annotators?
-<!-- This section describes the people or systems who created the annotations. -->
-[More Information Needed]
-#### Personal and Sensitive Information
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Dataset Card Authors [optional]
-[More Information Needed]
-## Dataset Card Contact
-[More Information Needed]

+# 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:
 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.
+---
+## 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.
+---
+# 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