Update README.md

README.md

@@ -60,12 +60,11 @@ dataset_summary: '
   '
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-# Dataset Card for AVM_Segmentation
-
-<!-- Provide a quick summary of the dataset. -->
-
+# Dataset Card for AVM_Segmentation# Dataset Card for AVM (Around View Monitoring) Semantic Segmentation Dataset
 
+![image/png](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.
@@ -97,130 +96,195 @@ session = fo.launch_app(dataset)
 
 ### Dataset Description
 
-<!-- Provide a longer summary of what this dataset is. -->
+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)
 
-- **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]
+* **License:** Please refer to the [original dataset repository](https://github.com/ChulhoonJang/avm_dataset) for license information (which currently has no License)
 
-### Dataset Sources [optional]
+### Dataset Sources
 
-<!-- Provide the basic links for the dataset. -->
+* **Original Repository:** [https://github.com/ChulhoonJang/avm_dataset](https://github.com/ChulhoonJang/avm_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]
+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 section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
+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
 
-<!-- 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]
+### 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
 
-<!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
+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
 
-<!-- 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]
+- **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?
 
-<!-- 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]
+The original dataset was produced by researchers developing autonomous parking systems, likely in an academic or industrial research setting.
 
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+### Annotations
 
-#### 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]
+#### 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?
 
-<!-- This section describes the people or systems who created the annotations. -->
-
-[More Information Needed]
+Information about specific annotators is not provided in the original dataset documentation.
 
-#### Personal and Sensitive Information
+## 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]
+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
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Known Limitations
 
-[More Information Needed]
+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
 
-### Recommendations
+### Technical Challenges
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- **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
 
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
+## Recommendations
 
-## Citation [optional]
+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
 
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 
-**BibTeX:**
+### Exploring the Dataset
+```python
+# View class distribution
+print(dataset.count_values("classes_present"))
 
-[More Information Needed]
+# Filter indoor vs outdoor
+indoor = dataset.match(F("environment.label") == "indoor")
+outdoor = dataset.match(F("environment.label") == "outdoor")
 
-**APA:**
+# Samples with multiple vehicles
+multi_vehicle = dataset.match(F("num_vehicles") > 2)
+```
 
-[More Information Needed]
+## Citation
 
-## Glossary [optional]
+### 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}
+}
+```
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
+### APA
+Jang, C., et al. (2020). AVM (Around View Monitoring) System Datasets for Auto Parking. GitHub. https://github.com/ChulhoonJang/avm_dataset
 
-[More Information Needed]
+## More Information
 
-## More Information [optional]
+### 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)
 
-[More Information Needed]
+### 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 [optional]
+## Dataset Card Authors
 
-[More Information Needed]
+- **FiftyOne Integration**: Harpreet Sahota (Voxel51)
+- **Original Dataset**: Chulhoon Jang and team
 
 ## Dataset Card Contact
 
-[More Information Needed]
+- **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.