在 DeepFace 中选择阈值

Question

是否有任何研究表明如何为 DeepFace 中的模型选择默认阈值？

def find_threshold(model_name: str, distance_metric: str) -> float:
    base_threshold = {"cosine": 0.40, "euclidean": 0.55, "euclidean_l2": 0.75}

    thresholds = {
        # "VGG-Face": {"cosine": 0.40, "euclidean": 0.60, "euclidean_l2": 0.86}, # 2622d
        "VGG-Face": {
            "cosine": 0.68,
            "euclidean": 1.17,
            "euclidean_l2": 1.17,
        },  # 4096d - tuned with LFW
        "Facenet": {"cosine": 0.40, "euclidean": 10, "euclidean_l2": 0.80},
        "Facenet512": {"cosine": 0.30, "euclidean": 23.56, "euclidean_l2": 1.04},
        "ArcFace": {"cosine": 0.68, "euclidean": 4.15, "euclidean_l2": 1.13},
        "Dlib": {"cosine": 0.07, "euclidean": 0.6, "euclidean_l2": 0.4},
        "SFace": {"cosine": 0.593, "euclidean": 10.734, "euclidean_l2": 1.055},
        "OpenFace": {"cosine": 0.10, "euclidean": 0.55, "euclidean_l2": 0.55},
        "DeepFace": {"cosine": 0.23, "euclidean": 64, "euclidean_l2": 0.64},
        "DeepID": {"cosine": 0.015, "euclidean": 45, "euclidean_l2": 0.17},
        "GhostFaceNet": {"cosine": 0.65, "euclidean": 35.71, "euclidean_l2": 1.10},
    }

    threshold = thresholds.get(model_name, base_threshold).get(distance_metric, 0.4)

    return threshold

如何在每种场景下选择一个理想的值？这是完全经验主义的东西吗？

我正在测试模型，在某些情况下，检测会产生错误的识别。

Answer 1

根据我自己的经验：是的，这完全是经验性的。