---
title: panoptic-quality
tags:
- evaluate
- metric
description: PanopticQuality score
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
emoji: 🖼️
---
# SEA-AI/PanopticQuality
This hugging face metric uses `seametrics.segmentation.PanopticQuality` under the hood to compute a panoptic quality score. It is a wrapper class for the torchmetrics class [`torchmetrics.detection.PanopticQuality`](https://lightning.ai/docs/torchmetrics/stable/detection/panoptic_quality.html).
## Getting Started
To get started with PanopticQuality, make sure you have the necessary dependencies installed. This metric relies on the `evaluate`, `seametrics` and `seametrics[segmentation]`libraries for metric calculation and integration with FiftyOne datasets.
### Basic Usage
```python
>>> import evaluate
>>> from seametrics.payload.processor import PayloadProcessor
>>> MODEL_FIELD = ["maskformer-27k-100ep"]
>>> payload = PayloadProcessor("SAILING_PANOPTIC_DATASET_QA",
>>> gt_field="ground_truth_det",
>>> models=MODEL_FIELD,
>>> sequence_list=["Trip_55_Seq_2", "Trip_197_Seq_1", "Trip_197_Seq_68"],
>>> excluded_classes=[""]).payload
>>> module = evaluate.load("SEA-AI/PanopticQuality")
>>> module.add_payload(payload, model_name=MODEL_FIELD[0])
>>> module.compute()
100%|██████████| 3/3 [00:03<00:00, 1.30s/it]
Added data ...
Start computing ...
Finished!
{'scores': {'MOTORBOAT': [0.18632257426639526,
0.698709617058436,
0.2666666805744171],
'FAR_AWAY_OBJECT': [0.0, 0.0, 0.0],
'SAILING_BOAT_WITH_CLOSED_SAILS': [0.0, 0.0, 0.0],
'SHIP': [0.3621737026917471, 0.684105846616957, 0.529411792755127],
'WATERCRAFT': [0.0, 0.0, 0.0],
'SPHERICAL_BUOY': [0.0, 0.0, 0.0],
'FLOTSAM': [0.0, 0.0, 0.0],
'SAILING_BOAT_WITH_OPEN_SAILS': [0.0, 0.0, 0.0],
'CONTAINER': [0.0, 0.0, 0.0],
'PILLAR_BUOY': [0.0, 0.0, 0.0],
'AERIAL_ANIMAL': [0.0, 0.0, 0.0],
'HUMAN_IN_WATER': [0.0, 0.0, 0.0],
'WOODEN_LOG': [0.0, 0.0, 0.0],
'MARITIME_ANIMAL': [0.0, 0.0, 0.0],
'WATER': [0.9397601008415222, 0.9397601008415222, 1.0],
'SKY': [0.9674496332804362, 0.9674496332804362, 1.0],
'LAND': [0.30757412078761204, 0.8304501533508301, 0.37037035822868347],
'CONSTRUCTION': [0.0, 0.0, 0.0],
'OWN_BOAT': [0.0, 0.0, 0.0],
'ALL': [0.14543579641409013, 0.21686712374464112, 0.16665520166095935]},
'numbers': {'MOTORBOAT': [6, 15, 18, 4.1922577023506165],
'FAR_AWAY_OBJECT': [0, 8, 9, 0.0],
'SAILING_BOAT_WITH_CLOSED_SAILS': [0, 2, 0, 0.0],
'SHIP': [9, 1, 15, 6.156952619552612],
'WATERCRAFT': [0, 9, 12, 0.0],
'SPHERICAL_BUOY': [0, 4, 22, 0.0],
'FLOTSAM': [0, 0, 1, 0.0],
'SAILING_BOAT_WITH_OPEN_SAILS': [0, 6, 0, 0.0],
'CONTAINER': [0, 0, 0, 0.0],
'PILLAR_BUOY': [0, 0, 9, 0.0],
'AERIAL_ANIMAL': [0, 0, 0, 0.0],
'HUMAN_IN_WATER': [0, 0, 0, 0.0],
'WOODEN_LOG': [0, 0, 0, 0.0],
'MARITIME_ANIMAL': [0, 0, 0, 0.0],
'WATER': [15, 0, 0, 14.096401512622833],
'SKY': [15, 0, 0, 14.511744499206543],
'LAND': [5, 9, 8, 4.15225076675415],
'CONSTRUCTION': [0, 0, 0, 0.0],
'OWN_BOAT': [0, 0, 8, 0.0],
'ALL': [50, 54, 102, 43.109607100486755]}}
```
## Metric Settings
The metric takes four optional input parameters: __label2id__, __stuff__, __per_class__ and __split_sq_rq__.
* `label2id: Dict[str, int]`: this dictionary is used to map string labels to an integer representation.
if not provided a default setting will be used:
`{'WATER': 0,
'SKY': 1,
'LAND': 2,
'MOTORBOAT': 3,
'FAR_AWAY_OBJECT': 4,
'SAILING_BOAT_WITH_CLOSED_SAILS': 5,
'SHIP': 6,
'WATERCRAFT': 7,
'SPHERICAL_BUOY': 8,
'CONSTRUCTION': 9,
'FLOTSAM': 10,
'SAILING_BOAT_WITH_OPEN_SAILS': 11,
'CONTAINER': 12,
'PILLAR_BUOY': 13,
'AERIAL_ANIMAL': 14,
'HUMAN_IN_WATER': 15,
'OWN_BOAT': 16,
'WOODEN_LOG': 17,
'MARITIME_ANIMAL': 18}
`
* `stuff: List[str]`: this list holds all string labels that belong to stuff.
if not provided a default setting will be used:
`
["WATER", "SKY", "LAND", "CONSTRUCTION", "ICE", "OWN_BOAT"]`
* `per_class: bool = True`: By default, the results are split up per class.
Setting this to False will aggregate the results:
* average the "scores"
* sum up the "numbers"
* `split_sq_rq: bool = True`: By default, the PQ-score is returned in three parts: the PQ score itself, and split into the segmentation quality (SQ) and recognition quality (RQ) part.
Setting this to False will return the PQ score only (PQ=RQ*SQ).
## Output Values
A dictionary containing the following keys:
* __scores__: This is a dictionary, that contains a key for each label, if `per_class == True`. Otherwise it only contains the key __all__.
For each key, it contains a list that holds the scores in the following order: PQ, SQ and RQ. If `split_sq_rq == False`, the list consists of PQ only.
* __numbers__: This is a dictionary, that contains a key for each label, if `per_class == True`. Otherwise it only contains the key __all__.
For each key, it contains a list that consists of four elements: TP, FP, FN and IOU:
* __TP__: number of true positive predictions
* __FP__: number of false positive predictions
* __FN__: number of false negative predictions
* __IOU__: sum of IOU of all TP predictions with ground truth
With all these values, it is possible to calculate the final scores.
## Further References
- **seametrics Library**: Explore the [seametrics GitHub repository](https://github.com/SEA-AI/seametrics/tree/main) for more details on the underlying library.
- **Torchmetrics**: https://lightning.ai/docs/torchmetrics/stable/detection/panoptic_quality.html
- **Understanding Metrics**: The Panoptic Segmentation task, as well as Panoptic Quality as the evaluation metric, were introduced [in this paper](https://arxiv.org/pdf/1801.00868.pdf).
## Contribution
Your contributions are welcome! If you'd like to improve SEA-AI/PanopticQuality or add new features, please feel free to fork the repository, make your changes, and submit a pull request.