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Create Feedback Examples From a Zip File

put https://api.einstein.ai/v2/vision/bulkfeedback

Adds feedback examples to the dataset associated with the specified object detection model. Available in Einstein Vision API version 2.0 and later.

Request Parameters

Name

Type

Description

Available Version

data

string

Local .zip file to upload. The maximum .zip file size you can upload from a local drive is 50 MB.

The images and an annotations.csv file with the label and bounding box information are contained in a .zip file, just like when you create a dataset from a .zip file.

2.0

modelId

string

ID of the model that misclassified the images. The feedback examples are added to the dataset associated with this model.

2.0

If a model returns an incorrect prediction for an image, you can use that image to improve the model. You do this by adding the image to the dataset and retraining it to update the model. The misclassified images that you add to the dataset are known as feedback. Use this API to add misclassified images with the correct labels to the dataset from which the model was created.

This call supports only datasets that have a type of image-detection.

Keep the following points in mind when creating feedback examples.

  • You pass in a modelId parameter, but the examples are added to the dataset from which the specified model was created.

  • The .zip file that contains the feedback images and annotations file follows the same format and structure as the .zip file you use to create a dataset. See the Object Detection Datasets section in Create a Dataset From a Zip File Asynchronously.

  • This API call checks for duplicate images in the .zip file that contains the feedback images. If the .zip file contains multiple image files that have the same contents, only the first of the duplicate files is uploaded.

  • The call also checks for duplicates between the images in the .zip file and the images already in the dataset. When the feedback examples and data from the annotations.csv file are merged, the API reconciles any differences by replacing earlier examples with the feedback images in the .zip file.

  • The maximum image file size is 5 MB.

  • Images must be no larger than 1,600 pixels high by 1,600 pixels wide. You can upload images that are larger, but training the dataset might fail.

  • The supported image file types are PNG, JPG, and JPEG.

  • This call is asynchronous, so the response has a status of UPLOADING.

  • Use the dataset ID and make a call to Get a Dataset to query when the upload is complete. When available is true and statusMsg is SUCCEEDED the upload of feedback examples is complete.

Response Body

Name

Type

Description

Available Version

available

boolean

Specifies whether the dataset is ready to be trained.

2.0

createdAt

date

Date and time that the dataset was created.

2.0

id

long

ID of the dataset to which the feedback examples are added.

2.0

labelSummary

object

Contains the labels array that contains all the labels for the dataset.

2.0

name

string

Name of the dataset.

2.0

object

string

Object returned; in this case, dataset.

2.0

statusMsg

string

Status of the dataset while feedback is being added. Valid values are:

  • FAILED: <message>—Creation of feedback examples failed.
  • SUCCEEDED—Creation of feedback examples is complete.
  • UPLOADING—Upload of feedback examples is in progress.

2.0

totalExamples

int

Total number of examples in the dataset.

2.0

totalLabels

int

Total number of labels in the dataset.

2.0

type

string

Type of dataset data. Feedback examples can be added via a .zip file only for object detection datasets, so this returns image-detection.

2.0

updatedAt

date

Date and time that the dataset was last updated.

2.0

Label Response Body

Name

Type

Description

Available Version

datasetId

long

ID of the dataset to which the label belongs.

2.0

id

long

ID of the label.

2.0

name

string

Name of the label.

2.0

numExamples

int

Number of examples that have the label. This is the number of examples before the feedback examples are added to the dataset.

2.0

Language