mobilenetv2/inference_config.json

{
  "backend": "timm",
  "model_name": "mobilenetv2_100.ra_in1k",
  "img_size": 224,
  "mean": [
    0.485,
    0.456,
    0.406
  ],
  "std": [
    0.229,
    0.224,
    0.225
  ],
  "labels": [
    "0",
    "1"
  ],
  "threshold": 0.5057958364486694,
  "input_name": "pixel_values",
  "output_name": "logits",
  "val_metrics": {
    "ap": 0.9999209860935525,
    "auc": 0.9999195624195624,
    "threshold": 0.5057958364486694,
    "per_label": {
      "report_by_label": {
        "0": {
          "precision": 1.0,
          "recall": 0.990990990990991,
          "f1-score": 0.995475113122172,
          "support": 111.0
        },
        "1": {
          "precision": 0.9911504424778761,
          "recall": 1.0,
          "f1-score": 0.9955555555555555,
          "support": 112.0
        }
      },
      "accuracy": 0.9955156950672646,
      "confusion_matrix": {
        "labels": [
          "0",
          "1"
        ],
        "matrix": [
          [
            110,
            1
          ],
          [
            0,
            112
          ]
        ]
      }
    },
    "threshold_best_f1_score": 0.9955555555550555
  },
  "test_metrics": {
    "ap": 0.9990159004614657,
    "auc": 0.999034749034749,
    "threshold": 0.5057958364486694,
    "per_label": {
      "report_by_label": {
        "0": {
          "precision": 0.9736842105263158,
          "recall": 0.9910714285714286,
          "f1-score": 0.9823008849557522,
          "support": 112.0
        },
        "1": {
          "precision": 0.9908256880733946,
          "recall": 0.972972972972973,
          "f1-score": 0.9818181818181818,
          "support": 111.0
        }
      },
      "accuracy": 0.9820627802690582,
      "confusion_matrix": {
        "labels": [
          "0",
          "1"
        ],
        "matrix": [
          [
            111,
            1
          ],
          [
            3,
            108
          ]
        ]
      }
    }
  }
}