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Importing Data & Annotations

Options to import data and annotations exist throughout the platform. Data can be imported during dataset creation or by adding to existing datasets, and annotations can be imported from the dataset tab of a project details page.

Importing Data

Data can be imported to the Innotescus platform in two ways - either through the dataset creation process, or by adding data to an existing dataset.

Sources

Data can be uploaded from your local machine, imported from Dropbox, Google Drive, and Amazon S3, or imported via a .csv or .txt file containing presigned urls for each image or video, in the format shown below.

"filename1.jpg", https://storage.googleapis.com/example-bucket/signature1
"filename2.jpg", https://storage.googleapis.com/example-bucket/signature2
"filename3.jpg", https://storage.googleapis.com/example-bucket/signature3
.
.

Accepted Image Formats

The Innotescus platform currently accepts jpg, png, jpeg, pjpeg, bmp, dicom, tiff, and jfif images smaller than 50MB.

Accepted Video Formats

The Innotescus platform currently accepts mp4, webm, mov, mpeg, wmv, avi, mkv, and ogg videos smaller than 1GB.

Importing Annotations

Annotations can be imported to a dataset to view, edit, and analyze in the Innotescus platform. For a complete overview of this process, see Importing Annotations in the 'Datasets' section.

Coco Imports

Coco imports have the exact same format as coco exports, and are only available for image datasets.

info
{
  "description": str,
  "date_created": datetime
}

images
{
  "license": int,
  "file_name": str,
  "width": int,
  "height": int,
  "id": int
}

categories
{
  "supercategory": str,
  "id": int,
  "name": str
}

annotations
{
  "image_id": int,
  "id": int,
  "category_id": int,
  "segmentation": [[x1, y1, x2, y2...][x1, y1, x2, y2...]]
  "area": int,
  "bbox": [x,y,width,height],
  "iscrowd": 0 or 1,
}

.csv Imports

.csv imports have the same format as .csv exports and are only available for image datasets. Each line in the .csv corresponds to a single annotation, and has the format shown below:

image name, width, height, task type, class, list of points, rotation

Task Type: Valid task types are Classification, Object Detection, Semantic Segmentation, and Instance Segmentation.

List of Points (optional): For segmentations, this resembles [x1, y1, x2, y2, x3, y3...]; multi-polygon segmentations are given by a list such as [[xa1, ya1, xa2, ya2, xa3, ya3...],[xb1, yb1, xb2, yb2, xb3, yb3...]] for a mask comprised of separate sub-masks a and b. For bounding boxes, this field contains [x1, y1, x2, y2] where [x1, y1] and [x2,y2] are opposing corners of the bounding box. This field is empty for classification tasks.

Rotation (optional): This is measured in radians, and ranges from -π to π with respect to the positive x-axis. Rotation is only added for object detection tasks with bounding box rotation enabled.

Dataloop Imports

Annotations can be imported to Innotescus in the Dataloop proprietary .json format, shown here.

Innotescus .json Imports

The Innotescus .json format can be used to import annotations to images with or without metadata. To import, select the containing folder, or the .json files themselves. See the full format definition in the exports section.

KITTI Imports

Annotations can be imported in the KITTI format. To import, select or click and drag the containing folder, a zip of the KITTI .txt files, or the files themselves.

LabelBox Imports

Annotations can be imported to Innotescus in the Labelbox proprietary .json format, shown here.

Mask Imports

Mask Imports for Images

Mask imports for images must contain the classes.txt file and individual masks, or a separate .zip or folder of masks, named for the image they correspond to, as shown below:

classes.txt
/masks
  /image_name_1
  /image_name_2
  /image_name_3
  /image_name_4
  /image_name_5
    ⠇

Mask Imports for Videos

Mask imports for videos follows the same format as above with a slight change - users must import mask files separately from the classes.txt file. Mask files must be imported as a zip file with a folder named for each video in the dataset, each containing numbered mask files that correspond to each annotated frame of the video, as shown below:

classes.txt
masks.zip
  /video_name_1
    /0.png
    /1.png
    /2.png
    ⠇
  /video_name_2
    /0.png
    /1.png
    /2.png
    ⠇
  /video_name_3
    /0.png
    /1.png
    /2.png
    ⠇

ScaleAI Imports

Annotations can be imported to Innotescus in the Scale AI proprietary .json format, shown here.

Monitoring Import Progress

Imports and Jobs

The Imports Tab

A log of every import to a project, both of data and annotations, is kept in the imports tab of the project. View every import along with its details in the table, and any associated errors.

The Jobs Dropdown

While an import is processing, its activity will be shown using the jobs icon. Clicking on the jobs icon will reveal in progress jobs, and includes a stop icon if you wish to end an in-progress job.