The following objective section includes information about data requirements, input/output schema file, and the format of the data import files (JSON Lines & CSV) that are defined by the schema.
Object detection
Data requirements
| General image requirements | |
|---|---|
| Supported file types |
|
| Types of images | AutoML models are optimized for photographs of objects in the real world. |
| Training image file size (MB) | 30MB maximum size. |
| Prediction image file* size (MB) | 1.5MB maximum size. |
| Image size (pixels) | 1024 pixels by 1024 pixels suggested maximum. For images much larger than 1024 pixels by 1024 pixels some image quality may be lost during Vertex AI's image normalization process. |
| Labels and bounding box requirements | |
|---|---|
| The following requirements apply to datasets used to train AutoML models. | |
| Label instances for training | 10 annotations (instances) minimum. |
| Annotation requirements | For each label you must have at least 10 images, each with at least one annotation (bounding box and the label). However, for model training purposes it's recommended you use about 1000 annotations per label. In general, the more images per label you have the better your model will perform. |
| Label ratio (most common label to least common label): | The model works best when there are at most 100x more images for the most common label than for the least common label. For model performance, it is recommended that you remove very low frequency labels. |
| Bounding box edge length | At least 0.01 * length of a side of an image. For example, a 1000 * 900 pixel image would require bounding boxes of at least 10 * 9 pixels. Bound box minium size: 8 pixels by 8 pixels. |
| The following requirements apply to datasets used to train AutoML or custom-trained models. | |
| Bounding boxes per distinct image | 500 maximum. |
| Bounding boxes returned from a prediction request | 100 (default), 500 maximum. |
| Training data and dataset requirements | |
|---|---|
| The following requirements apply to datasets used to train AutoML models. | |
| Training image characteristics | 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. |
| Internal image preprocessing | After images are imported, Vertex AI performs preprocessing on the data. The preprocessed images are the actual data used to train the model. Image preprocessing (resizing) occurs when the image's smallest edge is greater than 1024 pixels. In the case where the image's smaller side is greater than 1024 pixels, that smaller side is scaled down to 1024 pixels. The larger side and specified bounding boxes are both scaled down by the same amount as the smaller side. Consequently, any scaled down annotations (bounding boxes and labels) are removed if they are less than 8 pixels by 8 pixels. Images with a smaller side less than or equal to 1024 pixel are not subject to preprocessing resizing. |
| The following requirements apply to datasets used to train AutoML or custom-trained models. | |
| Images in each dataset | 150,000 maximum |
| Total annotated bounding boxes in each dataset | 1,000,000 maximum |
| Number of labels in each dataset | 1 minimum, 1,000 maximum |
YAML schema file
Use the following publicly accessible schema file to import image object detection annotations (bounding boxes and labels). 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_bounding_box_io_format_1.0.0.yaml
Full schema file
title: ImageBoundingBox description: > Import and export format for importing/exporting images together with bounding box 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`. boundingBoxAnnotations: type: array description: Multiple bounding box Annotations on the image. items: type: object description: > Bounding box anntoation. `xMin`, `xMax`, `yMin`, and `yMax` are relative to the image size, and the point 0,0 is in the top left of 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. xMin: description: The leftmost coordinate of the bounding box. type: number format: double xMax: description: The rightmost coordinate of the bounding box. type: number format: double yMin: description: The topmost coordinate of the bounding box. type: number format: double yMax: description: The bottommost coordinate of the bounding box. type: number format: double 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",
"boundingBoxAnnotations": [
{
"displayName": "OBJECT1_LABEL",
"xMin": "X_MIN",
"yMin": "Y_MIN",
"xMax": "X_MAX",
"yMax": "Y_MAX",
"annotationResourceLabels": {
"aiplatform.googleapis.com/annotation_set_name": "displayName",
"env": "prod"
}
},
{
"displayName": "OBJECT2_LABEL",
"xMin": "X_MIN",
"yMin": "Y_MIN",
"xMax": "X_MAX",
"yMax": "Y_MAX"
}
],
"dataItemResourceLabels": {
"aiplatform.googleapis.com/ml_use": "test/train/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 - object_detection.jsonl:
{"imageGcsUri": "gs://bucket/filename1.jpeg", "boundingBoxAnnotations": [{"displayName": "Tomato", "xMin": "0.3", "yMin": "0.3", "xMax": "0.7", "yMax": "0.6"}], "dataItemResourceLabels": {"aiplatform.googleapis.com/ml_use": "test"}}
{"imageGcsUri": "gs://bucket/filename2.gif", "boundingBoxAnnotations": [{"displayName": "Tomato", "xMin": "0.8", "yMin": "0.2", "xMax": "1.0", "yMax": "0.4"},{"displayName": "Salad", "xMin": "0.0", "yMin": "0.0", "xMax": "1.0", "yMax": "1.0"}], "dataItemResourceLabels": {"aiplatform.googleapis.com/ml_use": "training"}}
{"imageGcsUri": "gs://bucket/filename3.png", "boundingBoxAnnotations": [{"displayName": "Baked goods", "xMin": "0.5", "yMin": "0.7", "xMax": "0.8", "yMax": "0.8"}], "dataItemResourceLabels": {"aiplatform.googleapis.com/ml_use": "training"}}
{"imageGcsUri": "gs://bucket/filename4.tiff", "boundingBoxAnnotations": [{"displayName": "Salad", "xMin": "0.1", "yMin": "0.2", "xMax": "0.8", "yMax": "0.9"}], "dataItemResourceLabels": {"aiplatform.googleapis.com/ml_use": "validation"}}
...
CSV
CSV format:
[ML_USE],GCS_FILE_PATH,[LABEL],[BOUNDING_BOX]*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. Labels must start with a letter and only contain letters, numbers, and underscores.BOUNDING_BOX. A bounding box for an object in the image. Specifying a bounding box involves more than one column.
A.X_MIN,Y_MIN
B.X_MAX,Y_MIN
C.X_MAX,Y_MAX
D.X_MIN,Y_MAX
Each vertex is specified by x, y coordinate values. Coordinates are normalized float values [0,1]; 0.0 is X_MIN or Y_MIN, 1.0 is X_MAX or Y_MAX.
For example, a bounding box for the entire image is expressed as (0.0,0.0,,,1.0,1.0,,), or (0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0).
The bounding box for an object can be specified in one of two ways:
- Two vertices (two sets of x,y coordinates) that are diagonally opposite points of
the rectangle:
A.X_MIN,Y_MIN
C.X_MAX,Y_MAX
as shown in this example:
A,,C,
X_MIN,Y_MIN,,,X_MAX,Y_MAX,, - All four vertices specified as shown in:
X_MIN,Y_MIN,X_MAX,Y_MIN, X_MAX,Y_MAX,X_MIN,Y_MAX,
If the four specified vertices don't form a rectangle parallel to image edges, Vertex AI specifies vertices that do form such a rectangle.
- Two vertices (two sets of x,y coordinates) that are diagonally opposite points of
the rectangle:
Example CSV - object_detection.csv:
test,gs://bucket/filename1.jpeg,Tomato,0.3,0.3,,,0.7,0.6,,
training,gs://bucket/filename2.gif,Tomato,0.8,0.2,,,1.0,0.4,,
gs://bucket/filename2.gif
gs://bucket/filename3.png,Baked goods,0.5,0.7,0.8,0.7,0.8,0.8,0.5,0.8
validation,gs://bucket/filename4.tiff,Salad,0.1,0.2,,,0.8,0.9,,
...