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Artificial intelligence

Getting starting

The Artificial intelligence offers image and video recognition as a service. Whether you have one image or billions, you are only steps away from using artificial intelligence to recognize your visual content.The Artificial intelligenceis built around a simple idea. You send inputs (an image or video) to the service and it returns predictions.

Getting starting

Apparel

The ‘Apparel’ model recognizes more than 100 fashion-related concepts including clothing and accessories. This model is great for anyone building a fashion-related app such an e-commerce platform or a fashion curation site

Apparel

Celebrity

The ‘Celebrity’ model analyzes images and returns probability scores on the likelihood that the media contains the face(s) of over 10,000 recognized celebrities. This model is great for anyone building an app that relies on celebrity comparisons.

Celebrity

color

The ‘Color’ model returns density values for dominant colors present in images. Color predictions are‌‌ returned in hex format and also mapped to their‌‌ closest W3C counterparts. This model is great for anyone building an app where color is an important distinguisher

color

Demographics

The ‘Demographics’ model analyzes images and returns information on age, gender, and multicultural appearance for each detected face based on facial characteristics. This model is great for anyone building an app that relies on demographic information to understand users.

Demographics

Face Embedding

The ‘Face Embedding’ model analyzes images and returns numerical vectors that represent each detected face in the image in a 1024-dimensional space. The vector representation is computed by using Clarifai’s ‘Face Detection’ model. The vectors of visually similar faces will be close to each other in the 1024-dimensional space. The ‘Face Embedding’ model can be used for organizing, filtering, and ranking images according to visual similarity.

Face Embedding

Face Detection

The ‘Face Detection’ model returns probability scores on the likelihood that the image contains human faces and coordinate locations of where those faces appear with a bounding box. This model is great for anyone building an app that monitors or detects human activity

Face Detection

General

The ‘General’ model recognizes over 11,000 different concepts including objects, themes, moods, and more. This model is a great all-purpose solution for most visual recognition needs

General

Focus

The ‘Focus’ model analyzes an image and returns 1) the overall focus value (0-1) that represents the probability that there is an in-focus region within the image, and 2) a bounding box and focus density for every in-focus region within the image. This model is great for anyone building an app that relies on filtering out blurry images.

Focus

General Embedding

The ‘General Embedding’ model analyzes images and returns numerical vectors that represent the input images in a 1024-dimensional space. The vector representation is computed by using Artifiellcial intigence ‘General’ model. The vectors of visually similar images will be close to each other in the 1024-dimensional space. The ‘General Embedding’ model can be used for filtering, indexing, ranking, and organizing images according to visual similarity

General Embedding

Landscape Quality

The Landscape Quality Model analyzes images and returns probability scores that can help determine the technical quality of a photograph. This model can guide a photographer’s choices on whether a photograph will make a good print or internet content for their website or social media.

Landscape Quality

Logo

The ‘Logo’ model analyzes images and returns probability scores on the likelihood that the media contains the logos of over 500 recognized brand names. This model is great for anyone building an app that relies on detecting brand logos on images

Logo

Moderation

This model returns probability scores on the likelihood that an image contains concepts such as gore, drugs, explicit nudity, or suggestive nudity. It also returns the probability that the image is “safe,” meaning it does not contain the aforementioned four moderation categories. This model is often used to protect online businesses and communities from the trust & safety risks associated with user-generated content

Moderation

NSFW

The ‘NSFW’ (Not Safe For Work) model returns probability scores on the likelihood that an image contains nudity. This model is great for anyone trying to automatically moderate or filter offensive content from their platform

NSFW

Textures and Patterns

The Textures and Patterns model is designed to acquire and apply knowledge for recognizing textures/patterns in a two-dimensional image. It includes common textures (feathers, woodgrain), unique/fresh concepts (petrified wood, glacial ice), and overarching descriptive concepts (veined, metallic).

Textures and Patterns

Portrait Quality

The Portrait Quality Model analyzes images and returns probability scores that can help determine the technical quality of a photograph. This model can guide a photographer’s choices on whether a photograph will make a good print or internet content for their website or social media

Portrait Quality

Wedding

The ‘Wedding’ model recognizes over 400 concepts related to weddings including bride, groom, flowers, and more. This model is great for anyone building a wedding platform or curating wedding-related content.

Wedding