metadata

app_file: app.py
colorFrom: yellow
colorTo: green
description: 'TODO: add a description here'
emoji: 🐢
pinned: false
runme:
  id: 01HPS3ASFJXVQR88985QNSXVN1
  version: v3
sdk: gradio
sdk_version: 4.36.0
tags:
  - evaluate
  - metric
title: ref-metrics

How to Use

import evaluate
from seametrics.payload.processor import PayloadProcessor

payload = {}
module = evaluate.load("SEA-AI/ref-metrics")
res = module._compute(payload, max_iou=0.5, recognition_thresholds=[0.3, 0.5, 0.8])
print(res)

Output

"model_1": {
            "overall": {
                "all": {
                    "tp": 50,
                    "fp": 20,
                    "fn": 10,
                    "precision": 0.71,
                    "recall": 0.83,
                    "f1": 0.76
                },
                "small": {
                    "tp": 15,
                    "fp": 5,
                    "fn": 2,
                    "precision": 0.75,
                    "recall": 0.88,
                    "f1": 0.81
                },
                "medium": {
                    "tp": 25,
                    "fp": 10,
                    "fn": 5,
                    "precision": 0.71,
                    "recall": 0.83,
                    "f1": 0.76
                },
                "large": {
                    "tp": 10,
                    "fp": 5,
                    "fn": 3,
                    "precision": 0.67,
                    "recall": 0.77,
                    "f1": 0.71
                }
            },
            "per_sequence": {
                "sequence_1": {
                    "all": {
                        "tp": 30,
                        "fp": 15,
                        "fn": 7,
                        "precision": 0.67,
                        "recall": 0.81,
                        "f1": 0.73
                    },
                    "small": {
                        "tp": 10,
                        "fp": 3,
                        "fn": 1,
                        "precision": 0.77,
                        "recall": 0.91,
                        "f1": 0.83
                    },
                    "medium": {
                        "tp": 15,
                        "fp": 7,
                        "fn": 2,
                        "precision": 0.68,
                        "recall": 0.88,
                        "f1": 0.77
                    },
                    "large": {
                        "tp": 5,
                        "fp": 2,
                        "fn": 1,
                        "precision": 0.71,
                        "recall": 0.83,
                        "f1": 0.76
                    }
                }
            }
        },
        "model_2": {
            "overall": {
                "all": {
                    "tp": 60,
                    "fp": 25,
                    "fn": 15,
                    "precision": 0.71,
                    "recall": 0.80,
                    "f1": 0.75
                },
                "small": {
                    "tp": 20,
                    "fp": 6,
                    "fn": 3,
                    "precision": 0.77,
                    "recall": 0.87,
                    "f1": 0.82
                },
                "medium": {
                    "tp": 30,
                    "fp": 12,
                    "fn": 5,
                    "precision": 0.71,
                    "recall": 0.86,
                    "f1": 0.78
                },
                "large": {
                    "tp": 10,
                    "fp": 7,
                    "fn": 5,
                    "precision": 0.59,
                    "recall": 0.67,
                    "f1": 0.63
                }
            },
            "per_sequence": {
                "sequence_1": {
                    "all": {
                        "tp": 40,
                        "fp": 18,
                        "fn": 8,
                        "precision": 0.69,
                        "recall": 0.83,
                        "f1": 0.75
                    },
                    "small": {
                        "tp": 12,
                        "fp": 4,
                        "fn": 2,
                        "precision": 0.75,
                        "recall": 0.86,
                        "f1": 0.80
                    },
                    "medium": {
                        "tp": 20,
                        "fp": 8,
                        "fn": 3,
                        "precision": 0.71,
                        "recall": 0.87,
                        "f1": 0.78
                    },
                    "large": {
                        "tp": 8,
                        "fp": 6,
                        "fn": 3,
                        "precision": 0.57,
                        "recall": 0.73,
                        "f1": 0.64
                    }
                }
            }
        }
    }

Citations

@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}}

@article{milan2016mot16,
title={MOT16: A benchmark for multi-object tracking},
author={Milan, Anton and Leal-Taix{\'e}, Laura and Reid, Ian and Roth, Stefan and Schindler, Konrad},
journal={arXiv preprint arXiv:1603.00831},
year={2016}}