Prepare image training data for classification

This page describes how to prepare image training data for use in a Vertex AI dataset to train an image classification model.

The following objective sections include information about data requirements, the input/output schema file, and the format of the data import files (JSON Lines & CSV) that are defined by the schema.

Single-label classification

Data requirements

  • Training data: The following image formats are supported when training your model. After Vertex AI API preprocesses these imported images they serve as the data used to train a model. Maximum file size is 30MB.
    • JPEG
    • GIF
    • PNG
    • BMP
    • ICO
  • Prediction data: The following image formats are supported when requesting a prediction from (querying) your model. Maximum file size is 1.5MB.
    • JPEG
    • GIF
    • PNG
    • WEBP
    • BMP
    • TIFF
    • ICO

    Best practices for image data used to train AutoML models

    The following best practices apply to datasets that train models using AutoML.

  • AutoML models are optimized for photographs of objects in the real world.
  • The training data should be as close as possible to the data on which predictions are to be made. For example, if your use case involves blurry and low-resolution images (such as from a security camera), your training data should be composed of blurry, low-resolution images. In general, you should also consider providing multiple angles, resolutions, and backgrounds for your training images.
  • Vertex AI models can't generally predict labels that humans can't assign. So, if a human can't be trained to assign labels by looking at the image for 1-2 seconds, the model likely can't be trained to do it either.
  • We recommend about 1000 training images per label. The minimum per label is 10. In general it takes more examples per label to train models with multiple labels per image, and resulting scores are harder to interpret.
  • The model works best when there are at most 100x more images for the most common label than for the least common label. We recommend removing very low frequency labels.
  • Consider including a None_of_the_above label and images that don't match any of your defined labels. For example, for a flower dataset, include images of flowers outside of your labeled varieties, and label them as None_of_the_above.

YAML schema file

Use the following publicly accessible schema file to import single-label image classification annotations. This schema file dictates the format of the data input files. This file's structure follows the OpenAPI schema.

gs://google-cloud-aiplatform/schema/dataset/ioformat/image_classification_single_label_io_format_1.0.0.yaml

Full schema file

title: ImageClassificationSingleLabel
description: >
 Import and export format for importing/exporting images together with
 single-label classification annotation. Can be used in
 Dataset.import_schema_uri field.
type: object
required:
- imageGcsUri
properties:
 imageGcsUri:
   type: string
   description: >
     A Cloud Storage URI pointing to an image. Up to 30MB in size.
     Supported file mime types: `image/jpeg`, `image/gif`, `image/png`,
     `image/webp`, `image/bmp`, `image/tiff`, `image/vnd.microsoft.icon`.
 classificationAnnotation:
   type: object
   description: Single classification Annotation on the image.
   properties:
     displayName:
       type: string
       description: >
         It will be imported as/exported from AnnotationSpec's display name,
         i.e. the name of the label/class.
     annotationResourceLabels:
       description: Resource labels on the Annotation.
       type: object
       additionalProperties:
         type: string
 dataItemResourceLabels:
   description: Resource labels on the DataItem.
   type: object
   additionalProperties:
     type: string

Input files

JSON Lines

JSON on each line:



{
  "imageGcsUri": "gs://bucket/filename.ext",
  "classificationAnnotation": {
    "displayName": "LABEL",
    "annotationResourceLabels": {
        "aiplatform.googleapis.com/annotation_set_name": "displayName",
        "env": "prod"
      }
   },
  "dataItemResourceLabels": {
    "aiplatform.googleapis.com/ml_use": "training/test/validation"
  }
}

Field notes:

  • imageGcsUri - The only required field.
  • annotationResourceLabels - Can contain any number of key-value string pairs. The only system-reserved key-value pair is the following:
    • "aiplatform.googleapis.com/annotation_set_name" : "value"

    Where value is one of the display names of the existing annotation sets in the dataset.

  • dataItemResourceLabels - Can contain any number of key-value string pairs. The only system-reserved key-value pair is the following which specifies the machine learning use set of the data item:
    • "aiplatform.googleapis.com/ml_use" : "training/test/validation"

Example JSON Lines - image_classification_single_label.jsonl:



{"imageGcsUri": "gs://bucket/filename1.jpeg",  "classificationAnnotation": {"displayName": "daisy"}, "dataItemResourceLabels": {"aiplatform.googleapis.com/ml_use": "test"}}
{"imageGcsUri": "gs://bucket/filename2.gif",  "classificationAnnotation": {"displayName": "dandelion"}, "dataItemResourceLabels": {"aiplatform.googleapis.com/ml_use": "training"}}
{"imageGcsUri": "gs://bucket/filename3.png",  "classificationAnnotation": {"displayName": "roses"}, "dataItemResourceLabels": {"aiplatform.googleapis.com/ml_use": "training"}}
{"imageGcsUri": "gs://bucket/filename4.bmp",  "classificationAnnotation": {"displayName": "sunflowers"}, "dataItemResourceLabels": {"aiplatform.googleapis.com/ml_use": "training"}}
{"imageGcsUri": "gs://bucket/filename5.tiff",  "classificationAnnotation": {"displayName": "tulips"}, "dataItemResourceLabels": {"aiplatform.googleapis.com/ml_use": "validation"}}
...

CSV

CSV format:

[ML_USE],GCS_FILE_PATH,[LABEL]
List of columns
  • ML_USE (Optional) - For data split purposes when training a model. Use TRAINING, TEST, or VALIDATION. For more information about manual data splitting, see About data splits for AutoML models.
  • GCS_FILE_PATH - This field contains the Cloud Storage URI for the image. Cloud Storage URIs are case-sensitive.
  • LABEL (Optional) - Labels must start with a letter and only contain letters, numbers, and underscores.

Example CSV - image_classification_single_label.csv:

test,gs://bucket/filename1.jpeg,daisy
training,gs://bucket/filename2.gif,dandelion
gs://bucket/filename3.png
gs://bucket/filename4.bmp,sunflowers
validation,gs://bucket/filename5.tiff,tulips
...

Multi-label classification

Data requirements

  • Training data: The following image formats are supported when training your model. After Vertex AI API preprocesses these imported images they serve as the data used to train a model. Maximum file size is 30MB.
    • JPEG
    • GIF
    • PNG
    • BMP
    • ICO
  • Prediction data: The following image formats are supported when requesting a prediction from (querying) your model. Maximum file size is 1.5MB.
    • JPEG
    • GIF
    • PNG
    • WEBP
    • BMP
    • TIFF
    • ICO

    Best practices for image data used to train AutoML models

    The following best practices apply to datasets that train models using AutoML.

  • AutoML models are optimized for photographs of objects in the real world.
  • The training data should be as close as possible to the data on which predictions are to be made. For example, if your use case involves blurry and low-resolution images (such as from a security camera), your training data should be composed of blurry, low-resolution images. In general, you should also consider providing multiple angles, resolutions, and backgrounds for your training images.
  • Vertex AI models can't generally predict labels that humans can't assign. So, if a human can't be trained to assign labels by looking at the image for 1-2 seconds, the model likely can't be trained to do it either.
  • We recommend about 1000 training images per label. The minimum per label is 10. In general it takes more examples per label to train models with multiple labels per image, and resulting scores are harder to interpret.
  • The model works best when there are at most 100x more images for the most common label than for the least common label. We recommend removing very low frequency labels.
  • Consider including a None_of_the_above label and images that don't match any of your defined labels. For example, for a flower dataset, include images of flowers outside of your labeled varieties, and label them as None_of_the_above.

YAML schema file

Use the following publicly accessible schema file to import multi-label image classification annotations. This schema file dictates the format of the data input files. This file's structure follows the OpenAPI schema.

gs://google-cloud-aiplatform/schema/dataset/ioformat/image_classification_multi_label_io_format_1.0.0.yaml

Full schema file

title: ImageClassificationMultiLabel
description: >
 Import and export format for importing/exporting images together with
 multi-label classification annotations. Can be used in
 Dataset.import_schema_uri field.
type: object
required:
- imageGcsUri
properties:
 imageGcsUri:
   type: string
   description: >
     A Cloud Storage URI pointing to an image. Up to 30MB in size.
     Supported file mime types: `image/jpeg`, `image/gif`, `image/png`,
     `image/webp`, `image/bmp`, `image/tiff`, `image/vnd.microsoft.icon`.
 classificationAnnotations:
   type: array
   description: Multiple classification Annotations on the image.
   items:
     type: object
     description: Classification annotation.
     properties:
       displayName:
         type: string
         description: >
           It will be imported as/exported from AnnotationSpec's display name,
           i.e. the name of the label/class.
       annotationResourceLabels:
         description: Resource labels on the Annotation.
         type: object
         additionalProperties:
           type: string
 dataItemResourceLabels:
   description: Resource labels on the DataItem.
   type: object
   additionalProperties:
     type: string

Input files

JSON Lines

JSON on each line:


{
  "imageGcsUri": "gs://bucket/filename.ext",
  "classificationAnnotations": [
    {
      "displayName": "LABEL1",
      "annotationResourceLabels": {
        "aiplatform.googleapis.com/annotation_set_name":"displayName",
        "label_type": "flower_type"
      }
    },
    {
      "displayName": "LABEL2",
      "annotationResourceLabels": {
        "aiplatform.googleapis.com/annotation_set_name":"displayName",
        "label_type": "image_shot_type"
      }
    }
  ],
  "dataItemResourceLabels": {
    "aiplatform.googleapis.com/ml_use": "training/test/validation"
  }
}

Field notes:

  • imageGcsUri - The only required field.
  • annotationResourceLabels - Can contain any number of key-value string pairs. The only system-reserved key-value pair is the following:
    • "aiplatform.googleapis.com/annotation_set_name" : "value"

    Where value is one of the display names of the existing annotation sets in the dataset.

  • dataItemResourceLabels - Can contain any number of key-value string pairs. The only system-reserved key-value pair is the following which specifies the machine learning use set of the data item:
    • "aiplatform.googleapis.com/ml_use" : "training/test/validation"

Example JSON Lines - image_classification_multi_label.jsonl:



{"imageGcsUri": "gs://bucket/filename1.jpeg",  "classificationAnnotations": [{"displayName": "daisy"}, {"displayName": "full_shot"}], "dataItemResourceLabels": {"aiplatform.googleapis.com/ml_use": "test"}}
{"imageGcsUri": "gs://bucket/filename2.gif",  "classificationAnnotations": [{"displayName": "dandelion"}, {"displayName": "medium_shot"}], "dataItemResourceLabels": {"aiplatform.googleapis.com/ml_use": "training"}}
{"imageGcsUri": "gs://bucket/filename3.png",  "classificationAnnotations": [{"displayName": "roses"}, {"displayName": "extreme_closeup"}], "dataItemResourceLabels": {"aiplatform.googleapis.com/ml_use": "training"}}
{"imageGcsUri": "gs://bucket/filename4.bmp",  "classificationAnnotations": [{"displayName": "sunflowers"}, {"displayName": "closeup"}], "dataItemResourceLabels": {"aiplatform.googleapis.com/ml_use": "training"}}
{"imageGcsUri": "gs://bucket/filename5.tiff",  "classificationAnnotations": [{"displayName": "tulips"}, {"displayName": "extreme_closeup"}], "dataItemResourceLabels": {"aiplatform.googleapis.com/ml_use": "validation"}}
...

CSV

CSV format:

[ML_USE],GCS_FILE_PATH,[LABEL1,LABEL2,...LABELn]
List of columns
  • ML_USE (Optional) - For data split purposes when training a model. Use TRAINING, TEST, or VALIDATION. For more information about manual data splitting, see About data splits for AutoML models.
  • GCS_FILE_PATH - This field contains the Cloud Storage URI for the image. Cloud Storage URIs are case-sensitive.
  • LABEL (Optional) - Labels must start with a letter and only contain letters, numbers, and underscores.

Example CSV - image_classification_multi_label.csv:

test,gs://bucket/filename1.jpeg,daisy,full_shot
training,gs://bucket/filename2.gif,dandelion,medium_shot
gs://bucket/filename3.png
gs://bucket/filename4.bmp,sunflowers,closeup
validation,gs://bucket/filename5.tiff,tulips,extreme_closeup
...
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