Visual Classifier
Learn about our visual classifier model type
Input: Images and videos
Output: Concepts
Visual classifier is a type of deep fine-tuned model that allows you to classify images and video frames into a set of concepts. It helps you answer the question "What" or "Who" is in your data.
The visual classifier model type also comes with various templates that give you the control to choose the specific architecture used by your neural network, as well as define a set of hyperparameters you can use to fine-tune the way your model learns.
Visual classifiers are commonly used for various computer vision tasks, such as:
- Image classification: Categorizing images into different concepts, such as "cat", "dog", "car", or "person".
- Object detection: Finding and identifying objects in images, such as faces, cars, or traffic signs.
- Scene recognition: Identifying the scene in an image, such as a beach, a forest, or a city.
- Video analysis: Tracking objects and events in videos.
You may choose a visual classifier model type in cases where:
- Accuracy and the ability to carefully target solutions take priority over speed and ease of use.
- You need a classification model to learn new features not recognized by the existing Clarifai models. In that case, you may need to "deep fine-tune" your custom model and integrate it directly within your workflows.
- You have a custom-tailored dataset, accurate labels, and the expertise and time to fine-tune models.
Example use case
A large retailer is looking to find and remove listings for illegal objects and substances across thousands of listings that include user-generated data. A classification model allows the retailer to quickly find listings that violate their community rules, and remove them from the site.