You create the dataset from the .zip file called alpine.zip
, referenced by its URL. In the following command, replace <TOKEN>
with your JWT token and run the command. This command:
- Creates a dataset called
alpine
from the specified .zip file - Creates three labels specified in the annotations.csv file:
Alpine - Oat Cereal
,Alpine - Corn Flakes
, andAlpine - Bran Cereal
- Creates an example for each image specified in the annotations file. In this scenario there are 33 examples.
- Adds the specified labels from the annotations file to each image.
The type
parameter specifies that the new dataset is an object detection dataset.
curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" -H "Content-Type: multipart/form-data" -F "path=https://einstein.ai/images/alpine.zip" -F "type=image-detection" https://api.einstein.ai/v2/vision/datasets/upload
{
"id": 1004942,
"name": "alpine",
"createdAt": "2017-12-11T22:07:32.000+0000",
"updatedAt": "2017-12-11T22:07:32.000+0000",
"labelSummary": {
"labels": []
},
"totalExamples": 0,
"available": false,
"statusMsg": "UPLOADING",
"type": "image-detection",
"language": "N/A",
"numOfDuplicates": 0,
"object": "dataset"
}
This call is asynchronous, so you get the dataset ID back right away, but the API continues to load data into the dataset. Use the call to Get a Dataset to monitor the status of the upload. When available
is true
and statusMsg
is SUCCEEDED
, the upload is complete and the dataset is ready to be trained.
This cURL call gets the dataset.
curl -X GET -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" https://api.einstein.ai/v2/vision/datasets/<DATASET_ID>
{
"id": 1004942,
"name": "alpine",
"createdAt": "2017-12-07T22:54:41.000+0000",
"updatedAt": "2017-12-07T22:54:44.000+0000",
"labelSummary": {
"labels": [
{
"id": 39688,
"datasetId": 1004942,
"name": "Alpine - Oat Cereal",
"numExamples": 32
},
{
"id": 39689,
"datasetId": 1004942,
"name": "Alpine - Corn Flakes",
"numExamples": 30
},
{
"id": 39690,
"datasetId": 1004942,
"name": "Alpine - Bran Cereal",
"numExamples": 31
}
]
},
"totalExamples": 33,
"totalLabels": 3,
"available": true,
"statusMsg": "SUCCEEDED",
"type": "image-detection",
"language": "N/A",
"numOfDuplicates": 0,
"object": "dataset"
}
The .zip file used to create an object detection dataset must contain the images and an annotations.csv file. The .zip file must have a specific structure, and the annotations.csv file must also be in the required format.
See the Object Detection Datasets section in Create a Dataset From a Zip File Asynchronously for guidelines about the .zip file and the annotations file.
Updated less than a minute ago