取得影片物件追蹤模型評估結果
透過集合功能整理內容
你可以依據偏好儲存及分類內容。
使用 get_model_evaluation 方法,取得影片物件追蹤模型評估結果。
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程式碼範例
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[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],[],[],[],null,["# Get a model evaluation for video object tracking\n\nGets a model evaluation for video object tracking using the get_model_evaluation method.\n\nExplore further\n---------------\n\n\nFor detailed documentation that includes this code sample, see the following:\n\n- [Evaluate and iterate AutoML video object tracking models](/vertex-ai/docs/video-data/object-tracking/evaluate-model)\n\nCode sample\n-----------\n\n### Node.js\n\n\nBefore trying this sample, follow the Node.js setup instructions in the\n[Vertex AI quickstart using\nclient libraries](/vertex-ai/docs/start/client-libraries).\n\n\nFor more information, see the\n[Vertex AI Node.js API\nreference documentation](/nodejs/docs/reference/aiplatform/latest).\n\n\nTo authenticate to Vertex AI, set up Application Default Credentials.\nFor more information, see\n\n[Set up authentication for a local development environment](/docs/authentication/set-up-adc-local-dev-environment).\n\n /**\n * TODO(developer): Uncomment these variables before running the sample\n * (not necessary if passing values as arguments). To obtain evaluationId,\n * instantiate the client and run the following the commands.\n */\n // const parentName = `projects/${project}/locations/${location}/models/${modelId}`;\n // const evalRequest = {\n // parent: parentName\n // };\n // const [evalResponse] = await modelServiceClient.listModelEvaluations(evalRequest);\n // console.log(evalResponse);\n\n // const modelId = 'YOUR_MODEL_ID';\n // const evaluationId = 'YOUR_EVALUATION_ID';\n // const project = 'YOUR_PROJECT_ID';\n // const location = 'YOUR_PROJECT_LOCATION';\n\n // Imports the Google Cloud Model Service Client library\n const {ModelServiceClient} = require('https://cloud.google.com/nodejs/docs/reference/aiplatform/latest/overview.html');\n\n // Specifies the location of the api endpoint\n const clientOptions = {\n apiEndpoint: 'us-central1-aiplatform.googleapis.com',\n };\n\n // Instantiates a client\n const modelServiceClient = new https://cloud.google.com/nodejs/docs/reference/aiplatform/latest/overview.html(clientOptions);\n\n async function getModelEvaluationVideoObjectTracking() {\n // Configure the parent resources\n const name = `projects/${project}/locations/${location}/models/${modelId}/evaluations/${evaluationId}`;\n const request = {\n name,\n };\n\n // Create get model evaluation request\n const [response] = await modelServiceClient.getModelEvaluation(request);\n\n console.log('Get model evaluation video object tracking response');\n console.log(`\\tName : ${response.name}`);\n console.log(`\\tMetrics schema uri : ${response.metricsSchemaUri}`);\n console.log(`\\tMetrics : ${JSON.stringify(response.metrics)}`);\n console.log(`\\tCreate time : ${JSON.stringify(response.createTime)}`);\n console.log(`\\tSlice dimensions : ${response.sliceDimensions}`);\n }\n getModelEvaluationVideoObjectTracking();\n\n### Python\n\n\nBefore trying this sample, follow the Python setup instructions in the\n[Vertex AI quickstart using\nclient libraries](/vertex-ai/docs/start/client-libraries).\n\n\nFor more information, see the\n[Vertex AI Python API\nreference documentation](/python/docs/reference/aiplatform/latest).\n\n\nTo authenticate to Vertex AI, set up Application Default Credentials.\nFor more information, see\n\n[Set up authentication for a local development environment](/docs/authentication/set-up-adc-local-dev-environment).\n\n from google.cloud import aiplatform\n\n\n def get_model_evaluation_video_object_tracking_sample(\n project: str,\n model_id: str,\n evaluation_id: str,\n location: str = \"us-central1\",\n api_endpoint: str = \"us-central1-aiplatform.googleapis.com\",\n ):\n \"\"\"\n To obtain evaluation_id run the following commands where LOCATION\n is the region where the model is stored, PROJECT is the project ID,\n and MODEL_ID is the ID of your model.\n\n model_client = aiplatform.gapic.ModelServiceClient(\n client_options={\n 'api_endpoint':'LOCATION-aiplatform.googleapis.com'\n }\n )\n evaluations = model_client.list_model_evaluations(parent='projects/PROJECT/locations/LOCATION/models/MODEL_ID')\n print(\"evaluations:\", evaluations)\n \"\"\"\n # The AI Platform services require regional API endpoints.\n client_options = {\"api_endpoint\": api_endpoint}\n # Initialize client that will be used to create and send requests.\n # This client only needs to be created once, and can be reused for multiple requests.\n client = aiplatform.gapic.https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.model_service.ModelServiceClient.html(client_options=client_options)\n name = client.https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.model_service.ModelServiceClient.html#google_cloud_aiplatform_v1_services_model_service_ModelServiceClient_model_evaluation_path(\n project=project, location=location, model=model_id, evaluation=evaluation_id\n )\n response = client.https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.model_service.ModelServiceClient.html#google_cloud_aiplatform_v1_services_model_service_ModelServiceClient_get_model_evaluation(name=name)\n print(\"response:\", response)\n\nWhat's next\n-----------\n\n\nTo search and filter code samples for other Google Cloud products, see the\n[Google Cloud sample browser](/docs/samples?product=aiplatform)."]]