Model ID: 6a3dc529acf3f720a629cdc8c6ad41a9
Model Name: subject
Model Type: visual-segmenter
Model Type ID: embedding-classifier
The Subject Visual Segmenter returns an object mask for the identified subject and non-subject in an image, as well as the confidence score for the predicted object masks.
Gather valuable business insights from images, text and data using machine learning, natural language processing and computer vision.
Assign tags or categories to analyze text based on content. Build models for topic analysis, sentiment analysis, smart reply and more.
Identify unwanted content such as gore, drugs, explicit nudity or suggestive nudity.
Analyze images and return probability scores on the likelihood that the media contains the face(s) of over 10,000 recognized celebrities.
Analyze images and returns numerical vectors that represent each detected face in the image in a 1024-dimensional space.
Recognize textures and patterns in a two-dimensional image e.g., feathers, woodgrain, petrified wood, glacial ice and overarching descriptive concepts (veined, metallic).
Identify different levels of nudity in your visual data. Ideal for moderating and filtering offensive content from your platform.
Explore our pre-built, ready-to-use image recognition models to suit your specific needs.
The Room Type Model is an "embedding-classifier" which means that you will need to insert the model into a workflow and feed it embeddings from an embedding model for it to work. We recommend that you start with the embeddings from our general model.
Learn more about making Mesh Workflow predictions in our API Guide.