Create a dataset for training image object detection models

This page shows you how to create a Vertex AI dataset from your image data so you can start training object detection models. You can create a dataset using either the Google Cloud console or the Vertex AI API.

Create an empty dataset and import or associate your data

Google Cloud console

Use the following instructions to create an empty dataset and either import or associate your data.

In the Google Cloud console, in the Vertex AI section, go to the Datasets page.
Go to the Datasets page
Click Create to open the create dataset details page.
Modify the Dataset name field to create a descriptive dataset display name.
Select the Image tab.
Select object detection as your model's objective.
Select a region from the Region drop-down list.
Click Create to create your empty dataset, and advance to the data import page.
Choose one of the following options from the Select an import method section:
Upload data from your computer
1. In the Select an import method section, choose to upload data from your computer.
2. Click Select files and choose all the local files to upload to a Cloud Storage bucket.
3. In the Select a Cloud Storage path section click Browse to choose a Cloud Storage bucket location to upload your data to.
Upload an import file from your computer
1. Click Upload an import file from your computer .
2. Click Select files and choose the local import file to upload to a Cloud Storage bucket.
3. In the Select a Cloud Storage path section click Browse to choose a Cloud Storage bucket location to upload your file to.
Select an import file from Cloud Storage
1. Click Select an import file from Cloud Storage .
2. In the Select a Cloud Storage path section click Browse to choose the import file in Cloud Storage.
Click Continue .
Data import can take several hours, depending on the size of your data. You can close this tab and return to it later. You will receive an email when your data is imported.

API

In order to create a machine learning model you must first have a representative collection of data to train with. After importing data you can make modifications and start model training.

Create a dataset

Use the following samples to create a dataset for your data.

REST

Before using any of the request data, make the following replacements:

LOCATION : Region where the dataset will be stored. This must be a region that supports dataset resources. For example, us-central1 . See List of available locations .
PROJECT : Your project ID .
DATASET_NAME : Name for the dataset.

HTTP method and URL:

POST https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/datasets

Request JSON body:

{
  "display_name": " DATASET_NAME 
",
  "metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/image_1.0.0.yaml"
}

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell , which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list .

Save the request body in a file named request.json , and execute the following command:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/datasets"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list .

Save the request body in a file named request.json , and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/datasets" | Select-Object -Expand Content

You should see output similar to the following. You can use the OPERATION_ID in the response to get the status of the operation.

{
  "name": "projects/ PROJECT_NUMBER 
/locations/ LOCATION 
/datasets/ DATASET_ID 
/operations/ OPERATION_ID 
",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.CreateDatasetOperationMetadata",
    "genericMetadata": {
      "createTime": "2020-07-07T21:27:35.964882Z",
      "updateTime": "2020-07-07T21:27:35.964882Z"
    }
  }
}

Terraform

The following sample uses the google_vertex_ai_dataset Terraform resource to create an image dataset named image-dataset .

To learn how to apply or remove a Terraform configuration, see Basic Terraform commands .

  resource 
  
 "google_vertex_ai_dataset" 
  
 "image_dataset" 
  
 { 
  
 display_name 
  
 = 
  
 "image-dataset" 
  
 metadata_schema_uri 
  
 = 
  
 "gs://google-cloud-aiplatform/schema/dataset/metadata/image_1.0.0.yaml" 
  
 region 
  
 = 
  
 "us-central1" 
 }

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Java API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  import 
  
 com.google.api.gax.longrunning. OperationFuture 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. CreateDatasetOperationMetadata 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. Dataset 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. DatasetServiceClient 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. DatasetServiceSettings 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. LocationName 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.concurrent.ExecutionException 
 ; 
 import 
  
 java.util.concurrent.TimeUnit 
 ; 
 import 
  
 java.util.concurrent.TimeoutException 
 ; 
 public 
  
 class 
 CreateDatasetImageSample 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
 , 
  
 ExecutionException 
 , 
  
 TimeoutException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 project 
  
 = 
  
 "YOUR_PROJECT_ID" 
 ; 
  
 String 
  
 datasetDisplayName 
  
 = 
  
 "YOUR_DATASET_DISPLAY_NAME" 
 ; 
  
 createDatasetImageSample 
 ( 
 project 
 , 
  
 datasetDisplayName 
 ); 
  
 } 
  
 static 
  
 void 
  
 createDatasetImageSample 
 ( 
 String 
  
 project 
 , 
  
 String 
  
 datasetDisplayName 
 ) 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
 , 
  
 ExecutionException 
 , 
  
 TimeoutException 
  
 { 
  
  DatasetServiceSettings 
 
  
 datasetServiceSettings 
  
 = 
  
  DatasetServiceSettings 
 
 . 
 newBuilder 
 () 
  
 . 
 setEndpoint 
 ( 
 "us-central1-aiplatform.googleapis.com:443" 
 ) 
  
 . 
 build 
 (); 
  
 // Initialize client that will be used to send requests. This client only needs to be created 
  
 // once, and can be reused for multiple requests. After completing all of your requests, call 
  
 // the "close" method on the client to safely clean up any remaining background resources. 
  
 try 
  
 ( 
  DatasetServiceClient 
 
  
 datasetServiceClient 
  
 = 
  
  DatasetServiceClient 
 
 . 
 create 
 ( 
 datasetServiceSettings 
 )) 
  
 { 
  
 String 
  
 location 
  
 = 
  
 "us-central1" 
 ; 
  
 String 
  
 metadataSchemaUri 
  
 = 
  
 "gs://google-cloud-aiplatform/schema/dataset/metadata/image_1.0.0.yaml" 
 ; 
  
  LocationName 
 
  
 locationName 
  
 = 
  
  LocationName 
 
 . 
 of 
 ( 
 project 
 , 
  
 location 
 ); 
  
  Dataset 
 
  
 dataset 
  
 = 
  
  Dataset 
 
 . 
 newBuilder 
 () 
  
 . 
 setDisplayName 
 ( 
 datasetDisplayName 
 ) 
  
 . 
 setMetadataSchemaUri 
 ( 
 metadataSchemaUri 
 ) 
  
 . 
 build 
 (); 
  
 OperationFuture<Dataset 
 , 
  
 CreateDatasetOperationMetadata 
>  
 datasetFuture 
  
 = 
  
 datasetServiceClient 
 . 
  createDatasetAsync 
 
 ( 
 locationName 
 , 
  
 dataset 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Operation name: %s\n" 
 , 
  
 datasetFuture 
 . 
 getInitialFuture 
 (). 
  get 
 
 (). 
 getName 
 ()); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Waiting for operation to finish..." 
 ); 
  
  Dataset 
 
  
 datasetResponse 
  
 = 
  
 datasetFuture 
 . 
  get 
 
 ( 
 120 
 , 
  
 TimeUnit 
 . 
 SECONDS 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Create Image Dataset Response" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Name: %s\n" 
 , 
  
 datasetResponse 
 . 
  getName 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Display Name: %s\n" 
 , 
  
 datasetResponse 
 . 
  getDisplayName 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Metadata Schema Uri: %s\n" 
 , 
  
 datasetResponse 
 . 
  getMetadataSchemaUri 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Metadata: %s\n" 
 , 
  
 datasetResponse 
 . 
  getMetadata 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Create Time: %s\n" 
 , 
  
 datasetResponse 
 . 
  getCreateTime 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Update Time: %s\n" 
 , 
  
 datasetResponse 
 . 
  getUpdateTime 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Labels: %s\n" 
 , 
  
 datasetResponse 
 . 
  getLabelsMap 
 
 ()); 
  
 } 
  
 } 
 }

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Node.js API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  /** 
 * TODO(developer): Uncomment these variables before running the sample.\ 
 * (Not necessary if passing values as arguments) 
 */ 
 // const datasetDisplayName = "YOUR_DATASTE_DISPLAY_NAME"; 
 // const project = 'YOUR_PROJECT_ID'; 
 // const location = 'YOUR_PROJECT_LOCATION'; 
 // Imports the Google Cloud Dataset Service Client library 
 const 
  
 { 
 DatasetServiceClient 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/aiplatform 
' 
 ); 
 // Specifies the location of the api endpoint 
 const 
  
 clientOptions 
  
 = 
  
 { 
  
 apiEndpoint 
 : 
  
 'us-central1-aiplatform.googleapis.com' 
 , 
 }; 
 // Instantiates a client 
 const 
  
 datasetServiceClient 
  
 = 
  
 new 
  
  DatasetServiceClient 
 
 ( 
 clientOptions 
 ); 
 async 
  
 function 
  
 createDatasetImage 
 () 
  
 { 
  
 // Configure the parent resource 
  
 const 
  
 parent 
  
 = 
  
 `projects/ 
 ${ 
 project 
 } 
 /locations/ 
 ${ 
 location 
 } 
 ` 
 ; 
  
 // Configure the dataset resource 
  
 const 
  
 dataset 
  
 = 
  
 { 
  
 displayName 
 : 
  
 datasetDisplayName 
 , 
  
 metadataSchemaUri 
 : 
  
 'gs://google-cloud-aiplatform/schema/dataset/metadata/image_1.0.0.yaml' 
 , 
  
 }; 
  
 const 
  
 request 
  
 = 
  
 { 
  
 parent 
 , 
  
 dataset 
 , 
  
 }; 
  
 // Create Dataset Request 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 datasetServiceClient 
 . 
 createDataset 
 ( 
 request 
 ); 
  
 console 
 . 
 log 
 ( 
 `Long running operation: 
 ${ 
 response 
 . 
 name 
 } 
 ` 
 ); 
  
 // Wait for operation to complete 
  
 await 
  
 response 
 . 
 promise 
 (); 
  
 const 
  
 result 
  
 = 
  
 response 
 . 
 result 
 ; 
  
 console 
 . 
 log 
 ( 
 'Create dataset image response' 
 ); 
  
 console 
 . 
 log 
 ( 
 `Name : 
 ${ 
 result 
 . 
 name 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `Display name : 
 ${ 
 result 
 . 
 displayName 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `Metadata schema uri : 
 ${ 
 result 
 . 
 metadataSchemaUri 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `Metadata : 
 ${ 
 JSON 
 . 
 stringify 
 ( 
 result 
 . 
 metadata 
 ) 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `Labels : 
 ${ 
 JSON 
 . 
 stringify 
 ( 
 result 
 . 
 labels 
 ) 
 } 
 ` 
 ); 
 } 
 createDatasetImage 
 ();

Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python . For more information, see the Python API reference documentation .

The following sample uses the Vertex AI SDK for Python to both create a dataset and import data. If you run this sample code, then you can skip the Import data section of this guide.

This particular sample imports data for single-label classification. If your model has a different objective, then you must adjust the code.

  from 
  
 typing 
  
 import 
 List 
 , 
 Union 
 from 
  
 google.cloud 
  
 import 
 aiplatform 
 def 
  
 create_and_import_dataset_image_sample 
 ( 
 project 
 : 
 str 
 , 
 location 
 : 
 str 
 , 
 display_name 
 : 
 str 
 , 
 src_uris 
 : 
 Union 
 [ 
 str 
 , 
 List 
 [ 
 str 
 ]], 
 sync 
 : 
 bool 
 = 
 True 
 , 
 ): 
  
 """ 
 src_uris -- a string or list of strings, e.g. 
 ["gs://bucket1/source1.jsonl", "gs://bucket7/source4.jsonl"] 
 """ 
 aiplatform 
 . 
 init 
 ( 
 project 
 = 
 project 
 , 
 location 
 = 
 location 
 ) 
 ds 
 = 
 aiplatform 
 . 
 ImageDataset 
 . 
 create 
 ( 
 display_name 
 = 
 display_name 
 , 
 gcs_source 
 = 
 src_uris 
 , 
 import_schema_uri 
 = 
 aiplatform 
 . 
 schema 
 . 
 dataset 
 . 
 ioformat 
 . 
 image 
 . 
 single_label_classification 
 , 
 sync 
 = 
 sync 
 , 
 ) 
 ds 
 . 
 wait 
 () 
 print 
 ( 
 ds 
 . 
 display_name 
 ) 
 print 
 ( 
 ds 
 . 
 resource_name 
 ) 
 return 
 ds

Import data

After you create an empty dataset you can import your data into the dataset. If you used the Vertex AI SDK for Python to create the dataset, then you might have already imported data when you created the dataset. If so, you can skip this section.

REST

Before using any of the request data, make the following replacements:

LOCATION : Region where the dataset is located. For example, us-central1 .
PROJECT_ID : .
DATASET_ID : ID of the dataset.
IMPORT_FILE_URI : Path to the CSV or JSON Lines file in Cloud Storage that lists data items stored in Cloud Storage to use for model training; for import file formats and limitations, see Preparing image data .

HTTP method and URL:

POST https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/datasets/ DATASET_ID 
:import

Request JSON body:

{
  "import_configs": [
    {
      "gcs_source": {
        "uris": " IMPORT_FILE_URI 
"
      },
     "import_schema_uri" : "gs://google-cloud-aiplatform/schema/dataset/ioformat/image_bounding_box_io_format_1.0.0.yaml"
    }
  ]
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json , and execute the following command:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/datasets/ DATASET_ID 
:import"

PowerShell

Save the request body in a file named request.json , and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/datasets/ DATASET_ID 
:import" | Select-Object -Expand Content

You should see output similar to the following. You can use the OPERATION_ID in the response to get the status of the operation.

{
  "name": "projects/ PROJECT_NUMBER 
/locations/ LOCATION 
/datasets/ DATASET_ID 
/operations/ OPERATION_ID 
",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.ImportDataOperationMetadata",
    "genericMetadata": {
      "createTime": "2020-07-08T20:32:02.543801Z",
      "updateTime": "2020-07-08T20:32:02.543801Z"
    }
  }
}

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Java API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  import 
  
 com.google.api.gax.longrunning. OperationFuture 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. DatasetName 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. DatasetServiceClient 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. DatasetServiceSettings 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. GcsSource 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ImportDataConfig 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ImportDataOperationMetadata 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1. ImportDataResponse 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.Collections 
 ; 
 import 
  
 java.util.List 
 ; 
 import 
  
 java.util.concurrent.ExecutionException 
 ; 
 import 
  
 java.util.concurrent.TimeUnit 
 ; 
 import 
  
 java.util.concurrent.TimeoutException 
 ; 
 public 
  
 class 
 ImportDataImageObjectDetectionSample 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
 , 
  
 ExecutionException 
 , 
  
 TimeoutException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 project 
  
 = 
  
 "YOUR_PROJECT_ID" 
 ; 
  
 String 
  
 datasetId 
  
 = 
  
 "YOUR_DATASET_ID" 
 ; 
  
 String 
  
 gcsSourceUri 
  
 = 
  
 "gs://YOUR_GCS_SOURCE_BUCKET/path_to_your_image_source/[file.csv/file.jsonl]" 
 ; 
  
 importDataImageObjectDetectionSample 
 ( 
 project 
 , 
  
 datasetId 
 , 
  
 gcsSourceUri 
 ); 
  
 } 
  
 static 
  
 void 
  
 importDataImageObjectDetectionSample 
 ( 
  
 String 
  
 project 
 , 
  
 String 
  
 datasetId 
 , 
  
 String 
  
 gcsSourceUri 
 ) 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
 , 
  
 ExecutionException 
 , 
  
 TimeoutException 
  
 { 
  
  DatasetServiceSettings 
 
  
 datasetServiceSettings 
  
 = 
  
  DatasetServiceSettings 
 
 . 
 newBuilder 
 () 
  
 . 
 setEndpoint 
 ( 
 "us-central1-aiplatform.googleapis.com:443" 
 ) 
  
 . 
 build 
 (); 
  
 // Initialize client that will be used to send requests. This client only needs to be created 
  
 // once, and can be reused for multiple requests. After completing all of your requests, call 
  
 // the "close" method on the client to safely clean up any remaining background resources. 
  
 try 
  
 ( 
  DatasetServiceClient 
 
  
 datasetServiceClient 
  
 = 
  
  DatasetServiceClient 
 
 . 
 create 
 ( 
 datasetServiceSettings 
 )) 
  
 { 
  
 String 
  
 location 
  
 = 
  
 "us-central1" 
 ; 
  
 String 
  
 importSchemaUri 
  
 = 
  
 "gs://google-cloud-aiplatform/schema/dataset/ioformat/" 
  
 + 
  
 "image_bounding_box_io_format_1.0.0.yaml" 
 ; 
  
  GcsSource 
 
 . 
 Builder 
  
 gcsSource 
  
 = 
  
  GcsSource 
 
 . 
 newBuilder 
 (); 
  
 gcsSource 
 . 
  addUris 
 
 ( 
 gcsSourceUri 
 ); 
  
  DatasetName 
 
  
 datasetName 
  
 = 
  
  DatasetName 
 
 . 
 of 
 ( 
 project 
 , 
  
 location 
 , 
  
 datasetId 
 ); 
  
 List<ImportDataConfig> 
  
 importDataConfigList 
  
 = 
  
 Collections 
 . 
 singletonList 
 ( 
  
  ImportDataConfig 
 
 . 
 newBuilder 
 () 
  
 . 
 setGcsSource 
 ( 
 gcsSource 
 ) 
  
 . 
  setImportSchemaUri 
 
 ( 
 importSchemaUri 
 ) 
  
 . 
 build 
 ()); 
  
 OperationFuture<ImportDataResponse 
 , 
  
 ImportDataOperationMetadata 
>  
 importDataResponseFuture 
  
 = 
  
 datasetServiceClient 
 . 
  importDataAsync 
 
 ( 
 datasetName 
 , 
  
 importDataConfigList 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Operation name: %s\n" 
 , 
  
 importDataResponseFuture 
 . 
 getInitialFuture 
 (). 
  get 
 
 (). 
 getName 
 ()); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Waiting for operation to finish..." 
 ); 
  
  ImportDataResponse 
 
  
 importDataResponse 
  
 = 
  
 importDataResponseFuture 
 . 
  get 
 
 ( 
 300 
 , 
  
 TimeUnit 
 . 
 SECONDS 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Import Data Image Object Detection Response: %s\n" 
 , 
  
 importDataResponse 
 . 
 toString 
 ()); 
  
 } 
  
 } 
 }

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Node.js API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

  /** 
 * TODO(developer): Uncomment these variables before running the sample.\ 
 * (Not necessary if passing values as arguments) 
 */ 
 // const datasetId = "YOUR_DATASET_ID"; 
 // const gcsSourceUri = "YOUR_GCS_SOURCE_URI"; 
 // eg. "gs://<your-gcs-bucket>/<import_source_path>/[file.csv/file.jsonl]" 
 // const project = "YOUR_PROJECT_ID"; 
 // const location = 'YOUR_PROJECT_LOCATION'; 
 // Imports the Google Cloud Dataset Service Client library 
 const 
  
 { 
 DatasetServiceClient 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/aiplatform 
' 
 ); 
 // Specifies the location of the api endpoint 
 const 
  
 clientOptions 
  
 = 
  
 { 
  
 apiEndpoint 
 : 
  
 'us-central1-aiplatform.googleapis.com' 
 , 
 }; 
 const 
  
 datasetServiceClient 
  
 = 
  
 new 
  
  DatasetServiceClient 
 
 ( 
 clientOptions 
 ); 
 async 
  
 function 
  
 importDataImageObjectDetection 
 () 
  
 { 
  
 const 
  
 name 
  
 = 
  
 datasetServiceClient 
 . 
 datasetPath 
 ( 
 project 
 , 
  
 location 
 , 
  
 datasetId 
 ); 
  
 // Here we use only one import config with one source 
  
 const 
  
 importConfigs 
  
 = 
  
 [ 
  
 { 
  
 gcsSource 
 : 
  
 { 
 uris 
 : 
  
 [ 
 gcsSourceUri 
 ]}, 
  
 importSchemaUri 
 : 
  
 'gs://google-cloud-aiplatform/schema/dataset/ioformat/image_bounding_box_io_format_1.0.0.yaml' 
 , 
  
 }, 
  
 ]; 
  
 const 
  
 request 
  
 = 
  
 { 
  
 name 
 , 
  
 importConfigs 
 , 
  
 }; 
  
 // Create Import Data Request 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 datasetServiceClient 
 . 
 importData 
 ( 
 request 
 ); 
  
 console 
 . 
 log 
 ( 
 `Long running operation : 
 ${ 
 response 
 . 
 name 
 } 
 ` 
 ); 
  
 // Wait for operation to complete 
  
 await 
  
 response 
 . 
 promise 
 (); 
  
 console 
 . 
 log 
 ( 
  
 `Import data image object detection response : \ 
  
 ${ 
 JSON 
 . 
 stringify 
 ( 
 response 
 . 
 result 
 ) 
 } 
 ` 
  
 ); 
 } 
 importDataImageObjectDetection 
 ();

Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python . For more information, see the Python API reference documentation .

 def image_dataset_import_data_sample(
    project: str, location: str, src_uris: list, import_schema_uri: str, dataset_id: str
):
    aiplatform.init(project=project, location=location)

    ds = aiplatform.ImageDataset(dataset_id)

    ds = ds.import_data(
        gcs_source=src_uris, import_schema_uri=import_schema_uri, sync=True
    )

    print(ds.display_name)
    print(ds.name)
    print(ds.resource_name)
    return ds

Get operation status

Some requests start long-running operations that require time to complete. These requests return an operation name, which you can use to view the operation's status or cancel the operation. Vertex AI provides helper methods to make calls against long-running operations. For more information, see Working with long-running operations .

Prepare data

Train model

Create a dataset for training image object detection models Stay organized with collections Save and categorize content based on your preferences.

Create an empty dataset and import or associate your data

Google Cloud console

Upload data from your computer

Upload an import file from your computer

Select an import file from Cloud Storage

API

Create a dataset

REST

curl

PowerShell

Terraform

Java

Node.js

Python

Import data

REST

curl

PowerShell

Java

Node.js

Python

Get operation status

Create a dataset for training image object detection models