For this type of project, you can use the Product recognition in images preset.
You can use this preset to classify, rate or moderate content. It can also be used to label images for computer vision training.
Take a look at the example: the interface includes an image and tools for labeling objects and areas within it.
Note that validation, keyboard shortcuts, and task layout are already configured in this example.
View exampleIf this template doesn't meet your needs, see other examples in the Images section.
To add a detailed description to the task, use the label property of the field.image-annotation component.
To enhance Toloker's experience, you can highlight different types of data with colors using view.alert. You can place it in the view.list along with the field.image-annotation component.
In this example, the text is highlighted with a blue border.
To let Tolokers leave comments about the task or their response, add a text field using field.textarea.
If you need to categorize selected products, create labels for each category using the labels property of the field.image-annotation component. Note that if you use labels, you need to add at least two of them.
In this example, three buttons are used in the interface for selecting three categories of products: shoes, jeans, and dresses.
You can add a checkbox for reporting on problems with an image.
To ask Tolokers to clarify their choice if they selected the Cannot label the product checkbox, add the field.checkbox component and the helper.if component which contains field.radio-group.
If you want to allow labeling only after a certain condition has been met, use the disabled property of the field.image-annotation component.
In this example, a checkbox has been added so that users could label the photos where there is no product. Tolokers will be able to highlight products only if the Product in the photo option is activated.
Last updated: June 29, 2023