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Preparing image datasets is a critical and challenging task in any Machine Learning project.

Notate ML brings the power and usability of Apple's mobile devices to accelerate this task and deliver higher quality training data to your object detection model.

Create datasets
Add dataset

Touch `New` on the bottom toolbar to create a dataset...

...or select an existing dataset and then touch `Edit`.

Then edit the name and add labels.

Add Labels

Input labels one at a time...

Add one label

or as a comma-separated list...

Add labels using list

...or scan them in using the camera.

Scan labels

Swipe label left to edit or delete it.

Swipe left on label
Swipe right to finish editing label

Only unused labels can be deleted.

Add Images
Add images

Use `Import` or `Capture` to add images from your mobile device to a dataset.


Touch `Import` and allow access to camera roll.

Select up to 50 photos to import as a batch.

Once you have made your selections, touch `Add` to complete the import.

Import images from library


When prompted, grant access to your camera.

Grant access

Touch `Capture` to grab a frame or `Cancel` to go back.

Tap `✓` to add the captured frame to the dataset.

Capture image using camera

After adding images to your dataset, annotate them one at a time.

Images in dataset Image selected for annotation
Annotate Images

Zoom in and out of the image.

Crop to a region of interest.

Draw bounding boxes around target objects and label them.


Pinch with two fingers to zoom in or out.

Drag with two fingers to pant.

View visible area relative to the original image on the preview window.

Zoom into image while annotating


After zooming to a region of interest, tap `Crop` to crop the image to visible dimensions.

Cropping is possible, only when no objects have been tagged.

Crop image before annotating
Dimensions of image


Draw bounding boxes with your finger or a stylus.

Use the picker to assign a label.

Long press on a box to select it.

Bounding boxes

Change label while the box is selected, or tap `-` to delete the box.

Undo last action, tap `✓` to save and `x` to reset to last saved state.

Export Datasets

Select and open the dataset and tap `Export`.

Select the export schema - YOLO, Create ML or Auto ML.

Preview the files that will be exported.

Tap `✓` to download to a location accessible to your device.

Export dataset
Guidelines on cropping


Crop images before tagging them

We find it space-efficient to crop the images to specific regions of interest before tagging objects, unless your project requires feeding the entire image to the model for training.

The crop feature will not work if the given image has any bounding boxes present. Finish cropping your image before tagging.


Use a stylus

We find it helpful to use a stylus for drawing accurate bounding boxes, especially for very small objects.

Note that the minimum width and height of a bounding box in Notate ML is 15 pixels.

Using a stylus
Guidelines on cropping


Keep your datasets small

We find it optimal to keep the size of each dataset to under 1000 images.

Smaller datasets are easier to export. It is also easier to isolate issues with training data with smaller sets.

Splitting the project into multiple datasets also helps in cross-validation and testing.

Export and delete unused datasets to conserve device storage.

Privacy Notice

Last updated October 29 2023

Notate ML is a mobile application that helps create datasets for training "Object Detection" machine learning models.

It does not require or access any of your personal information.

It does not track or collect any usage data.

It stores the last visited dataset and dataset image in device local storage, and uses this to let you resume your work from the previous session.

It requires access to your device camera to capture photos for your datasets. It will ask for your permission to access the camera on first use of the feature. If you choose to decline, you can grant access at a later date using the device's "Settings" feature.

It provides the ability to import photos from your device library into your datasets. It uses Apple's "Photo Picker" feature that restricts the application's access to only those photos selected by you for import.

It uses crash logs made available through Apple App Store to diagnose issues that users may experience in the app.