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Create new dataset
Create a new dataset in a client.
** From 1st January 2020, one needs to call Process Upload API after this API call to complete preprocessing.
URL
Request
Path parameters
Parameter name | Parameter type | Description |
clientId | string | Client Id obtained from Deepen AI |
Request body
Property name | Property type | Description |
files | array | Array of metadata objects, each representing a file to be uploaded. Please see the example object below for the structure of the metadata object. For datasets of dataset_type ‘images’’, the metadata should be of a zip file of all images to be uploaded as part of the dataset. For datasets of dataset_type ‘video’, the metadata should be of an mp4 of the video to be uploaded as part of the dataset. |
dataset_format | string | The format in which the dataset is uploaded for ‘3d’ dataset types. Can take one of the following values :
Not required for dataset_type other than ‘3d’ |
dataset_type | string | The type of uploaded dataset:
|
dataset_name | string | Name of the uploaded dataset |
labelling_mode | string | Labelling format in the dataset:
|
labelling_profile_id | string | Labelling profile id This is the profile id of the profile from which to get the labelling instructions. |
labelling_profile | object | LabellingProfileResource indicating the labelling instructions. Either labelling_profile or labelling_profile_id is necessary to include labelling instructions with the dataset. |
tags | array | Array of TagResource |
user_scopes | array | Array of DatasetUserScopeResource |
user_group_scopes | array | Array of DatasetUserGroupScopeResource |
Example body
{
'dataset_name': 'testDataset', 'dataset_type': '3d', 'fps': 10, 'labelling_profile': {'pipeline_spec': [{'stage_id': 'Labelling', 'can_edit': True, 'skills': {'box': {}, 'point': {}, 'polygon': {}, 'lane': {}, '3d_bbox': {}, '3d_point': {}, '3d_instance_point': {}, '3d_polygon': {}, '3d_polyline': {}}}, {'stage_id': 'QA', 'can_edit': False, 'skills': {'box': {}, 'point': {}, 'polygon': {}, 'lane': {}, '3d_bbox': {}, '3d_point': {}, '3d_instance_point': {}, '3d_polygon': {}, '3d_polyline': {}}}], 'features': {'box': {'attributes': {}, 'label_category_ids': []}, 'lane': {'attributes': {}, 'label_category_ids': []}, 'polygon': {'attributes': {}, 'label_category_ids': []}, 'point': {'attributes': {}, 'label_category_ids': []}, '3d_bbox': {'attributes': {}, 'label_category_ids': []}, '3d_polyline': {'attributes': {}, 'label_category_ids': []}, '3d_polygon': {'attributes': {}, 'label_category_ids': []}, '3d_point': {'attributes': {}, 'label_category_ids': []}, '3d_instance_point': {'attributes': {}, 'label_category_ids': []}}, 'instructions': {'text': ''}, 'notification_url': ''}, 'labelling_mode': 'frame_by_frame', 'dataset_format': 'default', 'sensor_maker': None, 'files': [{'file_size': 16513471, 'file_type': 'application/zip'}]
} |
|
Response
Returns an array of resumable upload urls on successful completion
Response body
Property name | Property type | Description |
files | array | Array of metadata objects, each representing a file to be uploaded. Please see the example object below for the structure of the metadata object. Each metadata object has a Google cloud storage resumable upload url, to which the file has to be uploaded. Please see google cloud storage documentation at https://cloud.google.com/storage/docs/json_api/v2/how-tos/resumable-upload to learn how to upload the file using the url. |
dataset_id | string | The new dataset id created for the uploaded dataset |
Example
{
'dataset_id': 'BBZztvi0Z9TC', 'files': [{'resumable_upload_url': 'https://www.googleapis.com/upload/storage/v2/b/deepenai-dev-rannotateclientdata/o?uploadType=resumable&name=rannotate_client_uploaded_data/8cz4s0r1/defaultproject/BBZztvi0Z9TC/3d_data.zip&upload_id=AAANsUnQh_s03FsS-iGuvfD9n3ABpmvLxV8-Yi86g-QwGrtntj3HfYE8e_l1iDbeKAp06MXNGqvbotpgti1n3yjQEF0', 'signed_form_data': None, 'file_size': 16513471, 'file_type': 'application/zip'}]
} |
Please use the below command or follow this link to upload the data
curl -v -X PUT -H 'Expect:' --upload-file '<path to zip file>' '<resumable upload url>'
Once the dataset zip file is uploaded, you need to call the process uploaded data api to process the zip file and make it ready for labelling.
Last modified 1yr ago