#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the <a href="https://docs.voxel51.com/integrations/huggingface.html#loading-datasets-from-the-hub" target="_blank">Hugging Face integration docs</a>.

<a href="https://huggingface.co/datasets/Voxel51/AVM_Segmentation_train" target="_blank">![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-yellow)</a>

# Dataset Card for AVM (Around View Monitoring) Semantic Segmentation Dataset

![image/png](https://huggingface.co/datasets/Voxel51/AVM_Segmentation_train/resolve/main/avm_segmentation-mq.gif)

This repository provides a FiftyOne-compatible version of the AVM semantic segmentation dataset for autonomous parking systems, with enhanced metadata and visualization capabilities.

This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 6763 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("harpreetsahota/AVM_Segmentation_train")

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

## Dataset Details

### Dataset Description

The AVM dataset is a specialized computer vision dataset designed for training semantic segmentation models for autonomous parking systems. It contains bird’s-eye view images from around-view monitoring cameras with pixel-level annotations for parking space detection and obstacle avoidance.

* **Curated by:** Chulhoon Jang and team at [original repository](https://github.com/ChulhoonJang/avm_dataset)
* **FiftyOne Integration by:** Harpreet Sahota (Voxel51)
* **License:** Please refer to the [original dataset repository](https://github.com/ChulhoonJang/avm_dataset) for license information (which currently has no License)

### Dataset Sources

* **Original Repository:** [https://github.com/ChulhoonJang/avm_dataset](https://github.com/ChulhoonJang/avm_dataset)

## Uses

### Direct Use

This dataset is designed for:

- **Autonomous Parking Systems**: Training models to detect and navigate into parking spaces
- **Semantic Segmentation Research**: Benchmarking segmentation algorithms on fisheye/bird’s-eye view images
- **Parking Space Detection**: Identifying available vs occupied parking spots
- **Obstacle Detection**: Recognizing curbs, pillars, walls, and other vehicles
- **360° Surround View Systems**: Enhancing camera-based parking assistance features

### Out-of-Scope Use

This dataset should NOT be used for:

- Forward-facing autonomous driving (it’s specifically bird’s-eye view)
- General object detection (annotations are polygon-based for segmentation)
- High-speed navigation (designed for low-speed parking scenarios)
- Pedestrian detection (pedestrians are not annotated)

## Dataset Structure

### Overview

- **Total Images**: 6,763 (320 x 160 pixels)
- **Training Set**: 4,057 images
- **Test Set**: 2,706 images
- **Outdoor Images**: 3,614
- **Indoor Images**: 3,149

### Semantic Classes

The dataset contains 5 semantic classes with specific RGB color mappings:

|   Class | Description                   | RGB Color       | Hex Color       |
|---------|-------------------------------|-----------------|-----------------|
|       0 | Free Space (drivable area)    | [0, 0, 255]     | #0000FF (Blue)  |
|       1 | Marker (parking lines)        | [255, 255, 255] | #FFFFFF (White) |
|       2 | Vehicle (other cars)          | [255, 0, 0]     | #FF0000 (Red)   |
|       3 | Other (curbs, pillars, walls) | [0, 255, 0]     | #00FF00 (Green) |
|       4 | Ego Vehicle (camera car)      | [0, 0, 0]       | #000000 (Black) |

### FiftyOne Fields

When parsed into FiftyOne, each sample includes:

| Field                 | Type           | Description                                              |
|-----------------------|----------------|----------------------------------------------------------|
| `filepath`            | string         | Path to the image file                                   |
| `split`               | string         | “train” or “test”                                        |
| `sample_id`           | int            | Unique identifier from filename                          |
| `environment`         | Classification | “indoor” or “outdoor” (heuristic based on curb presence) |
| `parking_type`        | Classification | “perpendicular” or “parallel”                            |
| `slot_type`           | Classification | “closed”, “opened”, or “no_marker”                       |
| `polygon_annotations` | Polylines      | Normalized polygon coordinates for each object           |
| `ground_truth`        | Segmentation   | Pixel-level segmentation mask                            |
| `classes_present`     | list           | Classes present in the image                             |
| `num_markers`         | int            | Count of parking marker polygons                         |
| `num_vehicles`        | int            | Count of vehicle polygons                                |
| `has_curb`            | bool           | Whether curb is present                                  |
| `has_ego_vehicle`     | bool           | Whether ego vehicle is annotated                         |

## Dataset Creation

### Curation Rationale

The dataset was created to address the lack of bird’s-eye view datasets for autonomous parking systems. Most existing datasets focus on forward-facing cameras, but parking assistance requires a top-down perspective to accurately detect parking spaces and navigate safely.

### Source Data

#### Data Collection and Processing

- **Camera Setup**: Around View Monitoring (AVM) system with fisheye cameras
- **View Angle**: Bird’s-eye view (top-down perspective)
- **Resolution**: 320 x 160 pixels (optimized for embedded systems)
- **Environments**: Real parking lots (both indoor parking garages and outdoor lots)
- **Conditions**: Various lighting conditions, weather (sunny, cloudy, rainy)

#### Who are the source data producers?

The original dataset was produced by researchers developing autonomous parking systems, likely in an academic or industrial research setting.

### Annotations

#### Annotation Process

1. **Polygon Annotation**: Each object is annotated with precise polygon boundaries in YAML format
2. **Semantic Masks**: Ground truth masks are generated from polygon annotations
3. **Multi-polygon Support**: Multiple instances of the same class are supported (e.g., multiple vehicles)
4. **Coordinate System**: Polygons use image coordinates (0-319 x 0-159)

#### Who are the annotators?

Information about specific annotators is not provided in the original dataset documentation.

## Personal and Sensitive Information

The dataset contains images from parking lots but does not include:

- License plate information (resolution too low)
- Personally identifiable information
- Pedestrian annotations
- Location-specific information

## Bias, Risks, and Limitations

### Known Limitations

1. **Limited Resolution**: 320x160 pixels may not capture fine details
2. **Geographic Bias**: Dataset may be from specific geographic regions
3. **Weather Conditions**: Limited representation of extreme weather
4. **Vehicle Types**: May not include all vehicle types (trucks, motorcycles, etc.)
5. **Parking Styles**: Primarily perpendicular and parallel parking

### Technical Challenges

- **Indoor Reflections**: Reflected lights can be mistaken for parking markers
- **Fisheye Distortion**: Bird’s-eye view introduces geometric distortions
- **Class Imbalance**: Some classes (like curbs) appear less frequently

## Recommendations

1. **Augmentation**: Apply data augmentation to improve model robustness
2. **Validation**: Test models on diverse parking environments not in the dataset
3. **Resolution**: Consider upscaling techniques if higher resolution is needed
4. **Edge Cases**: Be aware that the dataset may not cover all parking scenarios

### Exploring the Dataset

```python
# View class distribution
print(dataset.count_values("classes_present"))

# Filter indoor vs outdoor
indoor = dataset.match(F("environment.label") == "indoor")
outdoor = dataset.match(F("environment.label") == "outdoor")

# Samples with multiple vehicles
multi_vehicle = dataset.match(F("num_vehicles") > 2)
```

## Citation

### BibTeX

```bibtex
@dataset{avm_dataset,
  title={AVM (Around View Monitoring) System Datasets for Auto Parking},
  author={Chulhoon Jang and others},
  year={2020},
  url={https://github.com/ChulhoonJang/avm_dataset}
}
```

### APA

Jang, C., et al. (2020). AVM (Around View Monitoring) System Datasets for Auto Parking. GitHub. https://github.com/ChulhoonJang/avm_dataset

## More Information

### Related Resources

- [Original Dataset Repository](https://github.com/ChulhoonJang/avm_dataset)
- [FiftyOne Documentation](https://docs.voxel51.com)
- Implementation code for semantic segmentation models (link in original repo)

### Dataset Statistics

- Average polygons per class:
  - Ego vehicle: 1.0 polygons (fixed position)
  - Markers: 2.6 polygons per image
  - Vehicles: 2.1 polygons per image
  - Curbs: 1.4 polygons per image (when present)

## Dataset Card Authors

- **FiftyOne Integration**: Harpreet Sahota (Voxel51)
- **Original Dataset**: Chulhoon Jang and team

## Dataset Card Contact

- **Original dataset**: See [original repository](https://github.com/ChulhoonJang/avm_dataset)

---

## Acknowledgments

Thanks to the original dataset creators for making this valuable resource available to the research community. The FiftyOne integration enhances the dataset’s usability for modern computer vision workflows.