Mendapatkan evaluasi model untuk pelacakan objek video
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Simpan dan kategorikan konten berdasarkan preferensi Anda.
Mendapatkan evaluasi model untuk pelacakan objek video menggunakan metode get_model_evaluation.
Contoh kode
Kecuali dinyatakan lain, konten di halaman ini dilisensikan berdasarkan Lisensi Creative Commons Attribution 4.0, sedangkan contoh kode dilisensikan berdasarkan Lisensi Apache 2.0. Untuk mengetahui informasi selengkapnya, lihat Kebijakan Situs Google Developers. Java adalah merek dagang terdaftar dari Oracle dan/atau afiliasinya.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],[],[],[],null,["Gets a model evaluation for video object tracking using the get_model_evaluation method.\n\nCode sample \n\nNode.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\nPython\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\nTo search and filter code samples for other Google Cloud products, see the\n[Google Cloud sample browser](/docs/samples?product=aiplatform)."]]