Upload images

You can upload images to Pixel Projects and Vector Projects only.

❗️

A project can’t contain more than 500,000 items (images, frames, videos, and documents).

Supported image file formats

SuperAnnotate supports the following image file formats: JPG, JPEG, PNG, WEBP, TIFF, BMP, and TIF.

Maximum image file size and resolution

Project type

Maximum image file size

Maximum image resolution

Pixel Project

100 MB

4 MP

Vector Project

100 MB

100 MP

Upload from computer

To upload images from your computer:

  1. In Data, click Upload.
  2. Choose Image.
  3. Drag and drop or choose files from your computer.
  4. Click Done.

📘

You can upload up to 2,000 images at a time. We recommend you upload up to 1,000 images at a time for a faster and smoother performance.

❗️

  • You can't upload more than 50,000 images, whether it is in a project's root or a folder. The remaining images will be skipped.

  • If you want to upload more than 50,000 images to your project, you can do so by creating multiple folders. For example, if you need to upload 100,000 images, you can create two folders and upload 50,000 images to each folder.

You can see the number of Uploaded, Skipped, and Failed images.

An image has the Skipped status if:

  • It has an existing name.
  • Its resolution exceeds 4 MP for Pixel Projects and 100 MP for Vector Projects .

An image has the Failed status if:

  • Its size exceeds 100 MB for Pixel Projects and Vector Projects.
  • SuperAnnotate doesn't support its file format.

Import from S3 bucket

To import images from your S3 bucket:

  1. In Data, click Upload.
  2. Choose Image.
  3. Click Import from S3 Bucket.
  4. Fill in the following fields: access key ID, secret access key, bucket name, and folder name. The folder name is optional.
  5. Click Test to see whether you have access to the S3 bucket or not.
  6. If you have access to the S3 bucket, click Start.

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If the image upload limit has been reached, the exceeding images won't be uploaded.

You can upload images using other methods:

Copy images

To copy an image or multiple images:

  1. Select an image or multiple images.
  2. Click Copy.
  3. Choose the destination folder.
  4. Check the Include annotations and statuses box if you want to copy the images with their annotations, statuses, pins, and assignees (optional).
  5. Click Copy.

🚧

  • You can't copy images and folders at the same time.
  • Images with existing names will be skipped.
  • Images that exceed the folder limit (50,000 images) won't be copied.

Move images

To move an image or multiple images:

  1. Select an image or multiple images.
  2. Click the Move button.
  3. Choose the destination folder.
  4. Click Move.

🚧

  • You can't move folders.
  • Images with existing names will be skipped and will remain in their source folder.
  • Images that exceed the folder limit (50,000 images) won't be copied.

Assign images

To assign an image:

  1. Select an image or multiple images.
  2. Click Assign.
  3. Choose a user or multiple users from the drop-down list or type their name(s) in the search bar.
  4. Click Apply.

🚧

You can assign images only to Annotators and QAs.

❗️

When you change the role of an Annotator or a QA, all the images assigned to them will be unassigned.

Unassign image

To unassign an image:

  1. Select an image.
  2. Click Assign.
  3. Click the X button next to the name of the user you want to unassign or click Remove assignments to unassign all the users.
  4. Click Apply.

Request images

To request images:

  • In Data, click Request Tasks.

OR

  • In the editor's image panel, click +.

📘

Only Annotators and QAs can request images. They'll be automatically assigned up to the number of images that you set in Request item number in Settings. The minimum number is 1 and the maximum number is 200 for both Pixel Projects and Vector Projects.

Received image status

  • Annotators receive images with the following statuses: Not started, In progress, and Returned.
  • QAs receive images with the Quality check status.

Image receiving order

Annotators and QAs receive the requested images in one of the following orders:

Condition

Image receiving order

If there's a folder

Annotators and QAs will first receive images from the folder, then they'll receive the images from the project’s root (if available) in ascending alphabetical order.

If there are multiple folders

Annotators and QAs will first receive images from the folders, then they'll receive the images from the project’s root (if available) in ascending alphabetical order.

If there's no folder

Annotators and QAs will receive images from the project’s root in ascending alphabetical order.

If the Annotator or QA has folders assigned to them

The Annotator or QA will first receive the images from the assigned folders, then they'll receive images from the other folders and the project’s root.

If the number of images that are available in the first folder is smaller than the number of Request item number in Settings

Annotators and QAs will receive the remaining images from the next folder.

Explanation:

Suppose a project has a request image number of 50 and contains the following items: Folder A (40 images) and Folder B (30 images). When the Annotator or QA request images, they'll receive 40 images from Folder A and then 10 images from Folder B.

❗️

  • Annotators and QAs have to finish working on the assigned images to be able to request more images.
  • An Annotator can't receive images from folders assigned to other Annotators.
  • A QA can't receive images from folders assigned to other QAs.

Run Smart Prediction

To run Smart Prediction on an image:

  1. In Data, select an image or multiple images.
  2. Click the Run Smart Prediction button.
  3. Choose a prediction model.
  4. Click Start prediction.

Prediction models

For Pixel Projects:

  • Instance segmentation (trained on COCO): Predicts your instance’s class label, bounding box, and binary mask. Use this model to detect countable objects, such as cats.
  • Panoptic segmentation (trained on COCO): Assigns all the pixels to classes. Use this model to detect countable objects, such as cars, and uncountable objects, such as the sky.
  • Semantic segmentation (trained on Cityscapes): Assigns all the pixels to classes. Use this model to annotate street scenes.

For Vector Projects:

  • Cars: Predicts vehicles.
  • Instance segmentation (trained on COCO): Predicts your instance’s class label, bounding box, and binary mask. Use this model to detect countable objects, such as cats.
  • Human pose keypoint detection (trained on COCO): Predicts human beings.
  • OCR (English text): Predicts texts in English.
  • Object detection (trained on COCO): Predicts objects using bounding boxes.

Run Smart Segmentation

To run Smart Segmentation:

  1. In Data, select an image or multiple images.
  2. Click the Run Smart Segmentation button.
  3. Choose a segmentation model.
  4. Click Start segmentation.

🚧

Smart Segmentation works only on Pixel Projects.

Image statuses

Status

Definition

Not started

The Annotator hasn't started working on the image.

In progress

An Annotator is working on the image.

Quality check

A QA is reviewing the image.

Returned

The image is returned to the Annotator for further review.

Completed

The Project Admin or Annotator approved the image.

Skipped

The image is complete, but it doesn't contain annotations.

Sort images

In Data, select the drop down arrow to sort the images by:

  • Image name
  • Status: Not started, In progress, Returned, Quality check, Completed, and Skipped.
  • Priority

Find image

To find an image, go to Data and type the image name in the search bar.

Filter images

In Data, click the filter icon and filter the images based on the:

  • Pinned status:
    • All: Pinned and unpinned images.
    • Pinned: Pinned images.
    • Not pinned: Unpinned images.
  • Approved/Disapproved status:
    • Approved: Approved images.
    • Disapproved: Disapproved images.
    • No approval: Images that are neither approved nor disapproved.
  • Assignee
  • Annotation status: Any, Not started, In progress, Quality check, Returned, Completed, and Skipped.
  • Prediction: Any, Not started, In progress, Completed, and Failed.
  • Segmentation: Any, Not started, In progress, Completed, and Failed (Pixel Projects only).

Click Apply to apply the filter.

To remove the filter, click Reset > Apply.

Delete images

To delete an image or multiple images:

  1. In Data, select an image or multiple images.
  2. Click the delete button.

🚧

Only Team Owners, Team Admins, Project Admins (with permission), and Customers can delete images.


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