This article applies to these versions of LandingLens:
            LandingLens            LandingLens on Snowflake
(see exceptions below)
LandingLens offers these project types:
  • Object Detection
  • Classification
  • Segmentation
  • Anomaly Detection
  • (not available in )
ProjectTypes

​
Object Detection

Use to identify one or more objects in an image. Object Detection can be used to identify one or more objects within an image. Object Detection trains based on the labeled pixels (pixels inside the bounding box). For more information, go to Object Detection.

​
Classification

Use to categorize (or “classify”) the content of an image. Classification identifies the image as a whole. Classification trains based on all pixels in an image. For more information, go to Classification.

​
Segmentation

Use to specify exact pixels to identify one or more regions within an image. For more information, go to Segmentation.

​
Anomaly Detection

Use to identify deviations from the norm, especially when you have few or no images of “abnormal” cases. For more information, go to Anomaly Detection.

​
Visual Prompting

Use to identify objects or areas in an image. You only need to label a few small areas for the model to detect the whole object or area. For more information, go to Visual Prompting.
is not available in .
Visual Prompting is in Beta. It might not function as well as our other production-ready features.

​
When to Use Each Project Type

Each Project Type is also designed for different use cases:
Project TypeWhen to UseExamples
Object Detection
  • You want to identify multiple objects in an image.
  • You want to identify one object in an image.
Identify multiple objects in an image.
  • Scratches and missing parts on a laptop
  • Apples and bananas
Identify one object in an image.
  • Deer
  • Person
  • License plate frame
Classification
  • You want to identify all the content within an image.
  • You want to distinguish one object type from another.
Identify all content within an image.
  • Image of a city
  • Image of a basketball
Distinguish one object type from another.
  • Screws vs. nails
  • Cats vs. dogs
Segmentation
  • You need to be precise.
  • You want to identify one or more regions in an image.
  • Identify cracks on computer monitors
Anomaly Detection
  • You want to identify deviations from the norm.
  • You have few or no images of “abnormal” cases.
  • Identify defects
  • Identify missing components on a product
  • Identify missing parts from a kit
  • Identifiy abnormalities in medical images
  • You want to create a model quickly.
  • You want to identify all regions in an image.
  • You are not using .
Identify objects with distinct textures.
  • Animals
  • Sand vs. rocks on a beach
Identify objects that have irregular shapes.
  • Regions of forests in satellite imagery
  • Scratches on a product