Upload from computer

To upload images from your computer:

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

After you're done uploading, you can see the number of uploaded, skipped, and failed images.

An image is skipped if:

  • It has an existing name.
  • Its resolution exceeds 4 MP for Pixel Projects and 100 MP for Vector Projects.
  • Its size exceeds 100 MB for Pixel Projects and Vector Projects.
  • It can’t be uploaded because the image upload limit has been reached.

An image is failed if:

  • Its resolution exceeds 100 MP.
  • SuperAnnotate doesn't support its file format.


You can't upload more than 50,000 images, whether it's in a root folder 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.


Supported image file formats

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

Import from S3 bucket

To import images from your S3 bucket:

  1. In Data, click Upload.
  2. Click Image.
  3. In Import from S3 bucket, enter the access key ID, secret access key, bucket name, and folder name (optional).
  4. Click Test to see whether you have access to the S3 bucket or not.
  5. If you have access to the S3 bucket, click Start.

CLI upload

Method 1: CLI upload

Use the following CLI command when you want to upload a large batch of images. Learn more here.

superannotatecli upload-images --project <project_name> --folder <folder_path>

Method 2: SDK function

You can also upload images with Python SDK. Learn more here.

uploaded, skipped, duplicate = sa.upload_images_from_folder_to_project(
        project = {{project_name}},
        folder_path = {{folder_path}}

Attach image URLs

You can import images by linking them from external storages. The linked images are displayed in SuperAnnotate, but they aren't stored on our local servers.

Step 1: Create a CSV file containing the image URLs

To attach image URLs, first create a CSV file that contains the URLs. Use this template CSV file as a reference.






Image URL



Image name


If the image name field is empty, a name ID will be generated based on the row ID and a random UUID.

Step 2: Attach image URLs via Python SDK

Link your images to the project with this SDK command.

superannotatecli attach-image-urls --project <project_name/folder_name> --attachments <csv_path> [--annotation_status <annotation_status>]
superannotate.attach_image_urls_to_project(project, attachments, annotation_status = “NotStarted”)

External storage project

After attaching image URLs, the projects will be considered as an external storage project.

In an external storage project, you can't:

  • Run smart segmentation and smart prediction.
  • Run model training.


There is no image size or resolution limit. Note that large or high-resolution images may impact the performance of the platform.

Data protection

Cloud storage

Signed URLs contain additional information such as expiration dates that gives you control over access to your data. You can use signed URLs to control multiple access parameters over your data on the cloud.

To make your data more secure, add accessibility restrictions to it including IP whitelisting and an expiration date.

Retrieve the signed URLs from your cloud storage:

You can use a VPN to give remote teams access to your cloud data:

On-prem data storage

SuperAnnotate can help Enterprise users install an on-prem infrastructure on their hardware to use their data stored in local storages. Contact us for more information.

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