SageMaker Ground Truthの画像ジョブの仕組みを調査した

NTT東日本クラソルクロスコラボレーション

中村佳央

2024.06.07

NTT東日本の中村です。

SageMaker Ground Truthのラベリングジョブを管理しているのですが、画像分類のタスクの配分とジョブの連鎖について調査してみました。ちなみに、動画フレームのジョブについては、こちらの記事で触れています。

概要

社内のプロジェクトでSageMaker Ground Truthのラベリングジョブを実行しており、同じようなテスト環境を構築しました。前回は動画フレームからGround Truthが自動的にスライスし、ラベル付けを行っていましたが、今回は予めスライス済の画像を用意し、データセットとして読み込ませる手法を取りました。

静止画のオブジェクト検出の特徴

動画フレームと静止画の２つのオブジェクト検出手法で、大まかな違いを紹介します。

「次を予測」等の、フレーム前後を補完するツールは存在しない

一連のスライスしたフレームをコマ送りでラベル付けを行いますが、「次を予測（Pregdict next）」というツールで現在のフレームの矩形を元に、次のフレームの矩形を予測する機能がついていました。静止画の場合、画像の前後関係は考慮されないため、このツールが存在しません。

動画フレーム

動画フレームのラベル付け画面です。

静止画

動画フレームには存在した、Predict Nextツールや、前のフレームを次のフレームにコピーする機能等が存在しません。

Gyazo

静止画群のラベリングで、データのラベル付けの自動化が使用できる

ジョブ作成時に、ラベル自動化のボタンが追加され、EC2で動作する機械学習を補助的に使用し、矩形の検出をサポートする機能が使えるようになります。元となる、ラベル付けの完了したデータを元に推論を行います。最小で1250程のデータが必要との事です。また、EC2で推論を行うため、追加コストも発生します。

自動データラベリングに許容される最小のオブジェクト数は 1,250 ですが、少なくとも 5,000 個のオブジェクトを指定することを強くお勧めします。

データフローオブジェクトの柔軟な調整が可能

動画フレームではNumberOfHumanWorkersPerDataObjectの値が１固定、つまり１つの動画フレームを処理できるワーカは一人と決まっていましたが、静止画ではこの値が変更可能となり、複数名が重複してラベル付けを行えるようになりました。

MaxConcurrentTaskCountの設定

今回は、MaxConcurrentTaskCountの調整も行ってみます。 MaxConcurrentTaskCountを指定することで、１タスク辺りに割り当てられる静止画の数を制御できます。

MaxConcurrentTaskCount は、人間のワーカーが同時にラベル付けできるデータオブジェクトの最大数を定義します。コンソールを使用する場合、このパラメータは 1,000 に設定されます。CreateLabelingJob を使用した場合は、このパラメータを 1～1,000 の範囲の任意の整数に設定できます。

例えば、100枚の静止画をデータセットとしたジョブが存在し、ワーカーが10人居た場合、１タスクを10枚に設定すると、一人１タスクで効率良くラベル付けを進めることができます。これが１タスク100枚になってしまうと、一人のワーカだけが忙しく、残りのワーカーは手が空いてしまうため、効率良く進めることが出来ません。

ただし、上記のコメントにもある通り、MaxConcurrentTaskCountはマネージメントコンソールからジョブを作成した場合、1000となり、CreateLabelingJobのAPI経由でのみ調整が可能です。途中で変更もできないようです（UpdateLaberingJobというAPIが存在しない）。

その他

output.manifestに出力されるデータ形式が異なるので、動画フレームからそのまま置き換えができる訳でもなく、一手間必要になります。

調査してみた

今回の調査のシナリオです。

静止画のオブジェクト検出でテストする
Amazon Cognitoを利用した Private WorkForce（三人）
NumberOfHumanWorkersPerDataObjectは1で行う（＝一つの画像を数人で作業することは無い）

MaxConcurrentTaskCountが1000の時の、ワーカーへのタスク割当の内容を確認する
1. データソースとして50の画像（1から50の数字が書かれている）を用意し、S3にアップロードしておく
2. 50の静止画をデータセットとして、Ground Truthのコンソールからジョブを作成する。（MaxConcurrentTaskCount=1000）
3. 各ワーカーが1タスクごとに何枚の画像を割り当てられているか、チェックする
MaxConcurrentTaskCountが10の時の、ワーカーへのタスク割当の内容を確認する
1. 50の静止画をデータセットとして、Ground TruthのCreateLabelingJobのAPI経由でジョブを作成する。（MaxConcurrentTaskCount=10）
2. 各ワーカーが1タスクごとに何枚の画像を割り当てられているか、チェックする
3. ジョブで「3」の数字に矩形を追加する（「33」は矩形が２つになる）
4. ジョブを完了し、output.manifestの内容を確認する
「２」で作成したジョブを連鎖して、Ground Truthのコンソールから調整ジョブを作成し、反映を確認する
1. 「２」で作成したジョブを連鎖して調整ジョブとし、Ground Truthのコンソールからジョブを作成する。このとき、「3と、4にも矩形を付ける」ように修正する。
2. ジョブで「4」の数字に矩形を追加する（結果、「33」、「34」、「43」、「44」は矩形が２つになる）
3. ジョブを完了し、output.manifestの内容を確認する
データセットに追加の画像を設定してジョブを連鎖し、output.manifestの反映を確認する
1. データソースとして、追加の50の画像（51-100の数字が書かれている）を用意し、S3にアップロードする
2. 「３」で完了したoutput.manifestを編集し、50の画像を追加データソースとして読み込ませる
3. 100枚の静止画をデータセットとして、「３」で作成したジョブを連鎖して、Ground Truthのコンソールからジョブを作成する。
4. ジョブで、追加された51-100の画像に「３」「４」の矩形を追加する
5. ジョブを完了し、output.manifestの内容を確認する

S3バケットはこのような階層になっています。input側のdatasetと、output側のjobのフォルダは、分離した方が管理しやすくなります。

.
├── dataset
│   ├── 1to50
│   │   ├── output_frame_0001.jpg
│   │   ├── output_frame_0002.jpg
│   │   └── ////etc
│   └── 50to100
│         ├── output_frame_0051.jpg
│         ├── output_frame_0052.jpg
│         └── ////etc
└── job
    ├── image-job-first （MaxConcurrentTaskCount=1000のジョブ）
    ├── image-job-second　（MaxConcurrentTaskCount=10のジョブ）
    ├── image-job-third-chain （連鎖した調整ジョブ）
    └── image-job-fourth-chain　（連鎖したデータセット追加ジョブ）

MaxConcurrentTaskCountが1000の時の、ワーカーへのタスク割当の内容を確認する

データソースとして50の画像（1から50の数字が書かれている）を用意し、S3にアップロードしておく

動画フレームの記事の動画を流用しています。

S3にアップロードします。

50の静止画をデータセットとして、Ground Truthのコンソールからジョブを作成する

マネジメントコンソールで作成するので、MaxConcurrentTaskCountは1000に設定されます。

効果は低そうですが、「自動データラベリング」を有効にします。下の項目は、NumberOfHumanWorkersPerDataObjectに相当するものです。

ジョブを開始しました。それぞれのワーカーにタスクが割り当てられていることが解ります。

各ワーカーが1タスクごとに何枚の画像を割り当てられているか、チェックする

3に矩形をつけていきますが、終わる気配が無いので、どんどん進めていきます。

48のデータオブジェクトを処理した時点で、タスクが完了しました。残り２のデータオブジェクトは、何もしていないtest2、test3ユーザが画面に表示している分と思われます。

test2、test3ユーザもデータオブジェクトを処理すると、ジョブが完了しました。

現状は、１つのタスクに未処理のものがある限り、同じタスクに参加しているユーザであれば、好きなだけ処理を行えることが解ります。

MaxConcurrentTaskCountが10の時の、ワーカーへのタスク割当の内容を確認する

MaxConcurrentTaskCountを10に制限してみます。同じデータセットから、CreateLabelingJobのAPI経由でジョブを作成します。

長々と書きましたが、MaxConcurrentTaskCount: 10である所だけがポイントです。

APIで実行する前に、ラベル情報のJSONと、ラベルUIのliquidテンプレートを用意し、S3にアップロードします。

S3://hoge/label/data.json

{"document-version":"2021-05-13","labels":[{"label":"three"}]}

S3://hoge/label/template.liquid

<img src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-wp-preserve="%3Cscript%20src%3D%22https%3A%2F%2Fassets.crowd.aws%2Fcrowd-html-elements.js%22%3E%3C%2Fscript%3E" data-mce-resize="false" data-mce-placeholder="1" class="mce-object" width="20" height="20" alt="&lt;script&gt;" title="&lt;script&gt;" />
<crowd-form>
  <crowd-bounding-box
    name="boundingBox"
    src="{{ task.input.taskObject | grant_read_access }}"
    header="3を囲んでください"
    labels="{{ task.input.labels | to_json | escape }}"
  >
    <full-instructions header="Bounding box instructions">
      <ol><li><strong>Inspect</strong> the image</li><li><strong>Determine</strong> if the specified label is/are visible in the picture.</li><li><strong>Outline</strong> each instance of the specified label in the image using the provided “Box” tool.</li></ol><ul><li>Boxes should fit tight around each object</li><li>Do not include parts of the object are overlapping or that cannot be seen, even though you think you can interpolate the whole shape.</li><li>Avoid including shadows.</li><li>If the target is off screen, draw the box up to the edge of the image.</li></ul><p><img src="https://a.b.cdn.console.awsstatic.com/a/v1/RXIU2KIKPUTEU7TQ5DQOX6O3DNMPRQXILCPUDLIQKIYSM7STA3OA/src/images/bounding-box-good-example.png" style="max-width:100%" alt="Bounding box good example"></p><h2><span style="color: rgb(0, 138, 0);">Good Example</span></h2><p><img src="[object Object]" style="max-width:100%" alt="Bounding box bad example"></p><h2><span style="color: rgb(230, 0, 0);">Bad Example</span></h2>
    </full-instructions>
    <short-instructions>
      <h3><span style="color: rgb(0, 138, 0);">Good example</span></h3><p>Enter description of a correct bounding box label</p><p><img src="https://a.b.cdn.console.awsstatic.com/a/v1/RXIU2KIKPUTEU7TQ5DQOX6O3DNMPRQXILCPUDLIQKIYSM7STA3OA/src/images/quick-instructions-example-placeholder.png" style="max-width:100%" alt="Add image here"></p><h3><span style="color: rgb(230, 0, 0);">Bad example</span></h3><p>Enter description of an incorrect bounding box label</p><p><img src="https://a.b.cdn.console.awsstatic.com/a/v1/RXIU2KIKPUTEU7TQ5DQOX6O3DNMPRQXILCPUDLIQKIYSM7STA3OA/src/images/quick-instructions-example-placeholder.png" style="max-width:100%" alt="Add image here"></p>
    </short-instructions>
  </crowd-bounding-box>
</crowd-form>

createLabelingJobs.mjs

import {
  SageMakerClient,
  CreateLabelingJobCommand,
} from "@aws-sdk/client-sagemaker";

// https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/sagemaker/command/CreateLabelingJobCommand/

const client = new SageMakerClient({ region: "ap-northeast-1" });
const input = {
  // CreateLabelingJobRequest
  LabelingJobName: "image-job-second", // required
  LabelAttributeName: "image-job-second", // required
  InputConfig: {
    DataSource: {
      S3DataSource: {
        ManifestS3Uri:
          "s3://hoge/dataset/1to50/dataset-20240605T174303.manifest", // required
      },
    },
  },
  OutputConfig: {
    S3OutputPath:
      "s3://hoge/job/", // required
  },
  RoleArn:
    "arn:aws:iam::hoge:role/service-role/AmazonSageMaker-ExecutionRole-20231019T170624", // required
  LabelCategoryConfigS3Uri:
    "s3://hoge/label/data.json",
  StoppingConditions: {
    MaxPercentageOfInputDatasetLabeled: 100,
  },
  LabelingJobAlgorithmsConfig: {
    LabelingJobAlgorithmSpecificationArn:
      "arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/object-detection", // required
    // InitialActiveLearningModelArn: "STRING_VALUE", // 前回のアクティブラーニングのARN
  },
  HumanTaskConfig: {
    UiConfig: {
      UiTemplateS3Uri:
        "s3://hoge/label/template.liquid",
    },
    // HumanTaskConfig
    WorkteamArn:
      "arn:aws:sagemaker:ap-northeast-1:hoge:workteam/private-crowd/inspection", // required
    PreHumanTaskLambdaArn:
      "arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox", // required
    TaskTitle: "3を矩形で囲んで下さい", // required
    TaskDescription: "Image object detection", // required
    NumberOfHumanWorkersPerDataObject: 1, // required
    TaskTimeLimitInSeconds: 28800, // required
    TaskAvailabilityLifetimeInSeconds: 864000,
    MaxConcurrentTaskCount: 10,
    AnnotationConsolidationConfig: {
      AnnotationConsolidationLambdaArn:
        "arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBox",
    },
  },
};
const command = new CreateLabelingJobCommand(input);
const response = await client.send(command);
console.log(response);

node --experimental-modules createLabelingJobs.mjs
{
  '$metadata': {
    httpStatusCode: 200,
    requestId: 'f7ab2edc-1ff0-4d47-903d-9bd81891e634',
    extendedRequestId: undefined,
    cfId: undefined,
    attempts: 1,
    totalRetryDelay: 0
  },
  LabelingJobArn: 'arn:aws:sagemaker:ap-northeast-1:hoge:labeling-job/image-job-second'
}

先ほどと同じ様に、３ユーザに分かれて処理を開始します。

test1ユーザが８件処理を行った所で、test1ユーザのタスクが完了しました。リロードしても、タスクが表示されません。

test2、test3ユーザが画面に出ていたデータオブジェクトを処理したところ、全員のタスクが完了になりました。 test1が８件、test2が1件、test3が１件、合計10件処理が完了しています。

３分ほど経過すると、全員に新しいタスクが割り当てられていました。

test1ユーザが4件、test2ユーザが3件、test3ユーザが3件、合計10件でタスクが完了しました。設定の通り、１つのタスク内の画像の数は、MaxConcurrentTaskCountにより制御できるようです。

output.manifestを確認しました。 JSON LINESの中で、annotationsの項目に、矩形が配列として格納されています。33だけ、配列の要素が２つ存在します。

output.manifest

{"source-ref":"s3://hoge/dataset/1to50/output_frame_0001.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T23:24:53.282150","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0002.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:12:14.429673","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0003.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[{"class_id":0,"top":196,"left":397,"height":207,"width":155}]},"image-job-second-metadata":{"objects":[{"confidence":0}],"class-map":{"0":"three"},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:04:46.606657","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0004.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:00:30.976891","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0005.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T23:24:53.285016","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0006.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:11:08.602026","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0007.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:04:46.609502","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0008.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T23:15:17.336632","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0009.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:12:14.432566","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0010.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T23:24:53.286465","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0011.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T09:58:24.652620","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0012.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T23:15:17.334639","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0013.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[{"class_id":0,"top":199,"left":473,"height":207,"width":152}]},"image-job-second-metadata":{"objects":[{"confidence":0}],"class-map":{"0":"three"},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:11:08.602794","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0014.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:12:14.430393","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0015.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T23:15:17.333969","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0016.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T23:25:57.839722","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0017.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T23:15:17.335960","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0018.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:12:14.428886","job-name":"labeling-job/image-job-second"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0019.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:12:14.431120","job-name":"labeling-job/image-job-second"}}
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作成したジョブを連鎖して、Ground Truthのコンソールから調整ジョブを作成する

先程のジョブを連鎖して、調整ジョブを作成します。調整ジョブとは、既につけられた教師データを確認し、必要であれば修正（矩形の位置を直すなど）を行う機能です。

MaxConcurrentTaskCountは1000で行います。ジョブの作成をコンソールから行います。 APIからも作成可能ですが、調整ジョブ用のUIテンプレートの作成等、かなり手間が掛かります。

Jobの名前を、image-job-third-chainで設定しました。自動データラベリングのチェックを外すと、既存ラベルの表示のオプションが表示されます。今回は３の矩形に加えて、４の矩形も追加します。既存ラベルを表示するようにします。

fourのラベルを追加しました。

ジョブで「4」の数字に矩形を追加する（結果、「33」、「34」、「43」、「44」は矩形が２つになる）

３のラベルは既に表示されています。領域が重ならないように、４に矩形を加えます。

ジョブを完了し、output.manifestの内容を確認する

s3://hoge/job/image-job-third-chain/manifests/output/output.manifest

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{"source-ref":"s3://hoge/dataset/1to50/output_frame_0041.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T09:58:24.656166","job-name":"labeling-job/image-job-second"},"image-job-third-chain":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[{"class_id":1,"top":203,"left":325,"height":196,"width":136}]},"image-job-third-chain-metadata":{"objects":[{"confidence":0}],"class-map":{"1":"four"},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-06T01:38:53.513903","job-name":"labeling-job/image-job-third-chain","adjustment-status":"adjusted"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0042.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T09:58:24.654741","job-name":"labeling-job/image-job-second"},"image-job-third-chain":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[{"class_id":1,"top":195,"left":297,"height":227,"width":180}]},"image-job-third-chain-metadata":{"objects":[{"confidence":0}],"class-map":{"1":"four"},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-06T01:34:10.638546","job-name":"labeling-job/image-job-third-chain","adjustment-status":"adjusted"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0043.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[{"class_id":0,"top":199,"left":473,"height":204,"width":149}]},"image-job-second-metadata":{"objects":[{"confidence":0}],"class-map":{"0":"three"},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T23:24:53.282918","job-name":"labeling-job/image-job-second"},"image-job-third-chain":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[{"class_id":0,"top":199,"left":473,"height":204,"width":149},{"class_id":1,"top":199,"left":317,"height":200,"width":152}]},"image-job-third-chain-metadata":{"objects":[{"confidence":0},{"confidence":0}],"class-map":{"0":"three","1":"four"},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-06T01:35:21.023027","job-name":"labeling-job/image-job-third-chain","adjustment-status":"adjusted"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0044.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:12:14.431844","job-name":"labeling-job/image-job-second"},"image-job-third-chain":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[{"class_id":1,"top":207,"left":328,"height":215,"width":149},{"class_id":1,"top":211,"left":477,"height":207,"width":164}]},"image-job-third-chain-metadata":{"objects":[{"confidence":0},{"confidence":0}],"class-map":{"1":"four"},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-06T01:36:32.796542","job-name":"labeling-job/image-job-third-chain","adjustment-status":"adjusted"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0045.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T23:25:57.839024","job-name":"labeling-job/image-job-second"},"image-job-third-chain":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[{"class_id":1,"top":207,"left":321,"height":207,"width":152}]},"image-job-third-chain-metadata":{"objects":[{"confidence":0}],"class-map":{"1":"four"},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-06T01:36:32.800151","job-name":"labeling-job/image-job-third-chain","adjustment-status":"adjusted"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0046.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:05:52.103672","job-name":"labeling-job/image-job-second"},"image-job-third-chain":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[{"class_id":1,"top":211,"left":328,"height":195,"width":137}]},"image-job-third-chain-metadata":{"objects":[{"confidence":0}],"class-map":{"1":"four"},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-06T01:38:53.517436","job-name":"labeling-job/image-job-third-chain","adjustment-status":"adjusted"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0047.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:04:46.608833","job-name":"labeling-job/image-job-second"},"image-job-third-chain":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[{"class_id":1,"top":203,"left":317,"height":200,"width":152}]},"image-job-third-chain-metadata":{"objects":[{"confidence":0}],"class-map":{"1":"four"},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-06T01:36:32.798711","job-name":"labeling-job/image-job-third-chain","adjustment-status":"adjusted"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0048.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:00:30.976177","job-name":"labeling-job/image-job-second"},"image-job-third-chain":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[{"class_id":1,"top":211,"left":325,"height":188,"width":136}]},"image-job-third-chain-metadata":{"objects":[{"confidence":0}],"class-map":{"1":"four"},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-06T01:36:32.794414","job-name":"labeling-job/image-job-third-chain","adjustment-status":"adjusted"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0049.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T23:15:17.335296","job-name":"labeling-job/image-job-second"},"image-job-third-chain":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[{"class_id":1,"top":215,"left":309,"height":188,"width":160}]},"image-job-third-chain-metadata":{"objects":[{"confidence":0}],"class-map":{"1":"four"},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-06T01:37:44.648064","job-name":"labeling-job/image-job-third-chain","adjustment-status":"adjusted"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0050.jpg","image-job-second":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:04:46.607393","job-name":"labeling-job/image-job-second"},"image-job-third-chain":{"image_size":[{"width":960,"height":540,"depth":3}],"annotations":[]},"image-job-third-chain-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-06T01:36:32.791702","job-name":"labeling-job/image-job-third-chain","adjustment-status":"unadjusted"}}

抜粋して、33番と34番のデータを確認します。

33番は、元々2つの矩形が存在し、調整ジョブでも何も作業しませんでした。 image-job-third-chain.annotationsで、最初の矩形が継承されているのが解ります。

output_frame_0033.jpg

{
    "source-ref": "s3://hoge/dataset/1to50/output_frame_0033.jpg",
    "image-job-second": {
        "annotations": [
            {
                "class_id": 0,
                "top": 203,
                "left": 317,
                "height": 207,
                "width": 156
            },
            {
                "class_id": 0,
                "top": 195,
                "left": 465,
                "height": 219,
                "width": 160
            }
        ]
    },
    "image-job-second-metadata": {
        //省略
    },
    "image-job-third-chain": {
        "annotations": [
            {
                "class_id": 0,
                "top": 203,
                "left": 317,
                "height": 207,
                "width": 156
            },
            {
                "class_id": 0,
                "top": 195,
                "left": 465,
                "height": 219,
                "width": 160
            }
        ]
    },
    "image-job-third-chain-metadata": {
        //省略
    }
}

34番は、元々1つの矩形が存在し、調整ジョブで追加の矩形が１つ追加されました。同じくimage-job-third-chain.annotationsで、継承された矩形と、新しい矩形で、２つの矩形が存在することが解ります。

output_frame_0034.jpg

{
    "source-ref": "s3://hoge/dataset/1to50/output_frame_0034.jpg",
    "image-job-second": {
        "annotations": [
            {
                "class_id": 0,
                "top": 191,
                "left": 313,
                "height": 227,
                "width": 156
            }
        ]
    },
    "image-job-second-metadata": {
        //省略
    },
    "image-job-third-chain": {
        "annotations": [
            {
                "class_id": 0,
                "top": 191,
                "left": 313,
                "height": 227,
                "width": 156
            },
            {
                "class_id": 1,
                "top": 191,
                "left": 473,
                "height": 232,
                "width": 145
            }
        ]
    },
    "image-job-third-chain-metadata": {
        //省略
    }
}

データセットに追加の画像を設定してジョブを連鎖し、output.manifestの反映を確認する

最後に、追加学習を想定して、50のラベル付けが必要な画像を追加してみます。

データソースとして、追加の50の画像（51-100の数字が書かれている）を用意し、S3にアップロードする

「３」で完了したoutput.manifestを編集し、50の画像を追加データソースとして読み込ませる

３のoutput.manifestは、「s3://hoge/job/image-job-third-chain/manifests/output/output.manifest」に保存されています。ここに追加データソースを50行加えて、リネームして保存しておきます。

代替マニフェストファイルを指定することはできません。前のジョブの出力マニフェストを手動で編集して新しい項目を追加してから、連鎖ジョブを開始します。 Amazon S3 URI を使用すると、Amazon S3 バケット内のマニフェストを格納している場所を見つけやすくなります。そこからマニフェストファイルをダウンロードして、コンピュータ上でローカルに編集し、新しいバージョンをアップロードして置き換えます。

s3://hoge/job/image-job-third-chain/manifests/output/custom-output.manifest

{"source-ref":"s3://hoge/dataset/1to50/output_frame_0001.jpg","image-job-second":{"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T23:24:53.282150","job-name":"labeling-job/image-job-second"},"image-job-third-chain":{"annotations":[]},"image-job-third-chain-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-06T01:34:10.637802","job-name":"labeling-job/image-job-third-chain","adjustment-status":"unadjusted"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0002.jpg","image-job-second":{"annotations":[]},"image-job-second-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:12:14.429673","job-name":"labeling-job/image-job-second"},"image-job-third-chain":{"annotations":[]},"image-job-third-chain-metadata":{"objects":[],"class-map":{},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-06T01:37:44.641007","job-name":"labeling-job/image-job-third-chain","adjustment-status":"unadjusted"}}
{"source-ref":"s3://hoge/dataset/1to50/output_frame_0003.jpg","image-job-second":{"annotations":[{"class_id":0,"top":196,"left":397,"height":207,"width":155}]},"image-job-second-metadata":{"objects":[{"confidence":0}],"class-map":{"0":"three"},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-05T10:04:46.606657","job-name":"labeling-job/image-job-second"},"image-job-third-chain":{"annotations":[{"class_id":0,"top":196,"left":397,"height":207,"width":155}]},"image-job-third-chain-metadata":{"objects":[{"confidence":0}],"class-map":{"0":"three"},"type":"groundtruth/object-detection","human-annotated":"yes","creation-date":"2024-06-06T01:35:21.023764","job-name":"labeling-job/image-job-third-chain","adjustment-status":"unadjusted"}}
....
//ここから下を追加
{"source-ref":"s3://hoge/dataset/51to100/output_frame_0051.jpg"}
{"source-ref":"s3://hoge/dataset/51to100/output_frame_0052.jpg"}
....

100の静止画をデータセットとして、「３」で作成したジョブを連鎖して、Ground Truthのコンソールからジョブを作成する

前回のジョブをクローンして、「image-job-fourth-chain」ジョブを作成します。ラベル属性名は前回のラベル属性名（一般的には前回のジョブ名）を使用しています。 manifestの1-50は、全て前回のラベル属性名が存在し、新規追加した51-100のデータにはラベル属性が存在しないので、結果として51-100のデータだけがラベル付けの対象となります。デフォルトのラベル属性名を使うと、自動で全てがラベル付の対象となります。

デフォルトでは、Ground Truth で選択されているラベルの属性名が使用されます。そのラベル属性名に接続されたデータを含まないデータオブジェクトはすべてラベル付けされます。マニフェストに存在しないラベル属性名を使用すると、データセット内のオブジェクトはすべて、ジョブで処理されます。

マニフェストファイルは、先ほど作成したcustom-output.manifest.jsonを使用します。

調整ジョブでオフになっていた自動データラベリングを、再度有効にします。

同時に、前回のラベル属性を指定することで、ラベル付けのツールに「three」「four」のラベルを表示することができます。

ジョブを作成すると、オブジェクトが100で認識されました。

ジョブで、追加された画像に「３」「４」の矩形を追加する

ジョブを開始すると、51-100の画像のみ表示されます。「３」と「４」にのみ矩形を追加する形で処理を進めていきます。

ジョブを完了し、output.manifestの内容を確認する

s3://hoge/job/image-job-fourth-chain/manifests/output/output.manifestの中身を確認します。抜粋して紹介します。

1-50のデータに変化はありませんでした。

74番の画像は、今回４の矩形を追加しています。指定したラベル属性名でアノテーションデータが作成されていることが解ります。

output_frame_0074.jpg

{
    "source-ref": "s3://hoge/dataset/51to100/output_frame_0074.jpg",
    "image-job-third-chain": {
        "image_size": [
            {
                "width": 960,
                "height": 540,
                "depth": 3
            }
        ],
        "annotations": [
            {
                "class_id": 1,
                "top": 199,
                "left": 469,
                "height": 223,
                "width": 160
            }
        ]
    },
    "image-job-third-chain-metadata": {... }
}

まとめ

SageMaker Ground Truthの画像検出について、調査を行いました。

createLabelingJobのAPIでMaxConcurrentTaskCountの値を調整すると、１タスク内の画像の枚数を1から1000の間で調整できる。
- タスクの粒度の設定となる。タスク毎の期限を細かく設定する時に有効と思われる
１つのタスクをワーカー全員でシェアする。タスク内の画像を全て処理するとタスク完了となり、新規タスクが作成される
output.manifestは連鎖を行うことにより、最新のジョブのannotationの中に、これまで全ての矩形の情報が蓄積されるようだ

MaxConcurrentTaskCountと、連鎖の仕組みについての理解が深まったと思います。

SageMaker Ground Truthの画像ジョブの仕組みを調査した

概要

静止画のオブジェクト検出の特徴

「次を予測」等の、フレーム前後を補完するツールは存在しない

動画フレーム

静止画

静止画群のラベリングで、データのラベル付けの自動化が使用できる

データフローオブジェクトの柔軟な調整が可能

MaxConcurrentTaskCountの設定

その他

調査してみた

MaxConcurrentTaskCountが1000の時の、ワーカーへのタスク割当の内容を確認する

データソースとして50の画像（1から50の数字が書かれている）を用意し、S3にアップロードしておく

50の静止画をデータセットとして、Ground Truthのコンソールからジョブを作成する

各ワーカーが1タスクごとに何枚の画像を割り当てられているか、チェックする

MaxConcurrentTaskCountが10の時の、ワーカーへのタスク割当の内容を確認する

作成したジョブを連鎖して、Ground Truthのコンソールから調整ジョブを作成する

ジョブで「4」の数字に矩形を追加する（結果、「33」、「34」、「43」、「44」は矩形が２つになる）

ジョブを完了し、output.manifestの内容を確認する

データセットに追加の画像を設定してジョブを連鎖し、output.manifestの反映を確認する

データソースとして、追加の50の画像（51-100の数字が書かれている）を用意し、S3にアップロードする

「３」で完了したoutput.manifestを編集し、50の画像を追加データソースとして読み込ませる

100の静止画をデータセットとして、「３」で作成したジョブを連鎖して、Ground Truthのコンソールからジョブを作成する

ジョブで、追加された画像に「３」「４」の矩形を追加する

ジョブを完了し、output.manifestの内容を確認する

まとめ

イベント

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SageMaker Ground Truthの画像ジョブの仕組みを調査した

概要

静止画のオブジェクト検出の特徴

「次を予測」等の、フレーム前後を補完するツールは存在しない

動画フレーム

静止画

静止画群のラベリングで、データのラベル付けの自動化が使用できる

データフローオブジェクトの柔軟な調整が可能

MaxConcurrentTaskCountの設定

その他

調査してみた

MaxConcurrentTaskCountが1000の時の、ワーカーへのタスク割当の内容を確認する

データソースとして50の画像（1から50の数字が書かれている）を用意し、S3にアップロードしておく

50の静止画をデータセットとして、Ground Truthのコンソールからジョブを作成する

各ワーカーが1タスクごとに何枚の画像を割り当てられているか、チェックする

MaxConcurrentTaskCountが10の時の、ワーカーへのタスク割当の内容を確認する

作成したジョブを連鎖して、Ground Truthのコンソールから調整ジョブを作成する

ジョブで「4」の数字に矩形を追加する（結果、「33」、「34」、「43」、「44」は矩形が２つになる）

ジョブを完了し、output.manifestの内容を確認する

データセットに追加の画像を設定してジョブを連鎖し、output.manifestの反映を確認する

データソースとして、追加の50の画像（51-100の数字が書かれている）を用意し、S3にアップロードする

「３」で完了したoutput.manifestを編集し、50の画像を追加データソースとして読み込ませる

100の静止画をデータセットとして、「３」で作成したジョブを連鎖して、Ground Truthのコンソールからジョブを作成する

ジョブで、追加された画像に「３」「４」の矩形を追加する

ジョブを完了し、output.manifestの内容を確認する

まとめ

イベント

EVENT【7/4リモート】 クラスメソッドのフリーランスエンジニア会社説明会〜AWS/Webアプリ/データ分析 案件特集 〜開催します

EVENT【7/12（金）沖縄】DevelopersIO 2024 OKINAWA #devio2024

EVENT【7/11（木）】AWSセキュリティ成熟度モデルを徹底解説！ 〜GitLabとAWSで実現する「アプリケーションセキュリティ」とは？〜

EVENT【7/31（水）大阪】DevelopersIO 2024 OSAKA #devio2024

EVENT【8/2（金）リモート】フリーランスの持続可能性を高めるには ‐ フリーランストーク#12

EVENT【7/18（木）リモート】猫好きエンジニア向け会社説明会を開催します

EVENT【7/23（火）リモート】クラスメソッドの会社説明会を開催します

EVENT【7/11（木）リモート】クラスメソッドの会社説明会を開催します

EVENT【7/5（金）札幌】DevelopersIO 2024 SAPPORO Business Lab #devio2024

EVENT【6/24（月）日比谷+オンライン】 クラスメソッドとLINEヤフーが語る開発ノウハウ

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EVENT【7/4リモート】クラスメソッドのフリーランスエンジニア会社説明会〜AWS/Webアプリ/データ分析案件特集〜開催します

EVENT【7/11（木）】AWSセキュリティ成熟度モデルを徹底解説！〜GitLabとAWSで実現する「アプリケーションセキュリティ」とは？〜

EVENT【6/24（月）日比谷+オンライン】クラスメソッドとLINEヤフーが語る開発ノウハウ