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解释评估清单快照
评估清单快照包含有关测试结果的详细信息。快照包含每个预测的置信度评级。此外，还包含图像的实际分类与预测分类的比较（真正例、真负例、假正例或假负例）。
这些文件是快照，因为只包含可用于测试和训练的图像。无法验证的图像（例如格式错误的图像）不包含在清单中。可从 DescribeProjectVersions 返回的 TestingDataResult 对象获取测试快照的位置。可从 DescribeProjectVersions 返回的 TrainingDataResult 对象获取训练快照的位置。
快照采用 G SageMaker round Truth 清单输出格式，添加了字段以提供其他信息，例如检测的二进制分类结果。以下代码段显示了其他字段。
"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：清单快照中
rekognition-custom-labels-evaluation-details字段格式的版本。is-true-positive... ：基于置信度分数与标签最小阈值的比较结果对预测进行的二进制分类。
is-present-in-ground-trut h — 如果模型所做的预测存在于用于训练的地面真相信息中，则为 True，否则为 false。该值不是基于置信度分数是否超过模型计算的最小阈值确定的。
ground-truth-labeling-jobs— 清单行中用于训练的地面真相字段列表。
有关 G SageMaker round Truth 清单格式的信息，请参阅输出。
下面是一个示例测试清单快照，其中显示了用于图像分类和物体检测的指标。
// 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" } }
