評估清單檔案快照

評估清單檔案快照會包含測試結果的詳細資訊。快照會包括每個預測的可信度評分。它還會包括和影像實際分類相比的預測分類 (相符、不相符、誤報或漏報)。

由於只包含可用於測試和訓練的影像，因此這些檔案會是快照。無法驗證的影像 (例如格式錯誤的影像) 不會包含在清單檔案中。測試快照位置可從由 DescribeProjectVersions 傳回的 TestingDataResult 物件存取。訓練快照位置可從由 DescribeProjectVersions 傳回的 TrainingDataResult 物件存取。

快照集採用 SageMaker Ground Truth 資訊清單輸出格式，並新增欄位以提供其他資訊，例如偵測的二進位分類結果。下列程式碼片段會顯示其他欄位。

"rekognition-custom-labels-evaluation-details": {
    "version": 1,
    "is-true-positive": true,
    "is-true-negative": false,
    "is-false-positive": false,
    "is-false-negative": false,
    "is-present-in-ground-truth": true
    "ground-truth-labelling-jobs": ["rekognition-custom-labels-training-job"]
}

• version — 清單檔案快照中的 rekognition-custom-labels-evaluation-details 欄位格式的版本。

• is-true-positive... — 根據可信度分數與標籤最小閾值比較結果的預測二進制分類。

• is-present-in-ground-truth — 如果模型所做的預測存在於用於訓練的 Ground Truth 資訊中，即為真，否則即為偽。此值的根據並非可信度分數是否超過模型所計算的最小閾值。

• ground-truth-labeling-jobs — 清單檔案行中用於訓練的 Ground Truth 欄位清單。

如需有關 SageMaker Ground Truth 資訊清單格式的資訊，請參閱輸出。

以下是測試清單檔案快照的範例，其中會顯示影像分類和物件偵測的指標。

// For image classification
{
  "source-ref": "s3://test-bucket/dataset/beckham.jpeg",
  "rekognition-custom-labels-training-0": 1,
  "rekognition-custom-labels-training-0-metadata": {
    "confidence": 1.0,
    "job-name": "rekognition-custom-labels-training-job",
    "class-name": "Football",
    "human-annotated": "yes",
    "creation-date": "2019-09-06T00:07:25.488243",
    "type": "groundtruth/image-classification"
  },
  "rekognition-custom-labels-evaluation-0": 1,
  "rekognition-custom-labels-evaluation-0-metadata": {
    "confidence": 0.95,
    "job-name": "rekognition-custom-labels-evaluation-job",
    "class-name": "Football",
    "human-annotated": "no",
    "creation-date": "2019-09-06T00:07:25.488243",
    "type": "groundtruth/image-classification",
    "rekognition-custom-labels-evaluation-details": {
      "version": 1,
      "ground-truth-labelling-jobs": ["rekognition-custom-labels-training-job"],
      "is-true-positive": true,
      "is-true-negative": false,
      "is-false-positive": false,
      "is-false-negative": false,
      "is-present-in-ground-truth": true
    }
  }
}


// For object detection
{
  "source-ref": "s3://test-bucket/dataset/beckham.jpeg",
  "rekognition-custom-labels-training-0": {
    "annotations": [
      {
        "class_id": 0,
        "width": 39,
        "top": 409,
        "height": 63,
        "left": 712
      },
      ...
    ],
    "image_size": [
      {
        "width": 1024,
        "depth": 3,
        "height": 768
      }
    ]
  },
  "rekognition-custom-labels-training-0-metadata": {
    "job-name": "rekognition-custom-labels-training-job",
    "class-map": {
      "0": "Cap",
      ...
    },
    "human-annotated": "yes",
    "objects": [
      {
        "confidence": 1.0
      },
      ...
    ],
    "creation-date": "2019-10-21T22:02:18.432644",
    "type": "groundtruth/object-detection"
  },
  "rekognition-custom-labels-evaluation": {
    "annotations": [
      {
        "class_id": 0,
        "width": 39,
        "top": 409,
        "height": 63,
        "left": 712
      },
      ...
    ],
    "image_size": [
      {
        "width": 1024,
        "depth": 3,
        "height": 768
      }
    ]
  },
  "rekognition-custom-labels-evaluation-metadata": {
    "confidence": 0.95,
    "job-name": "rekognition-custom-labels-evaluation-job",
    "class-map": {
      "0": "Cap",
      ...
    },
    "human-annotated": "no",
    "objects": [
      {
        "confidence": 0.95,
        "rekognition-custom-labels-evaluation-details": {
          "version": 1,
          "ground-truth-labelling-jobs": ["rekognition-custom-labels-training-job"],
          "is-true-positive": true,
          "is-true-negative": false,
          "is-false-positive": false,
          "is-false-negative": false,
          "is-present-in-ground-truth": true
        }
      },
      ...
    ],
    "creation-date": "2019-10-21T22:02:18.432644",
    "type": "groundtruth/object-detection"
  }
}