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Einstein's deep learning APIs enable you to build AI-powered apps using Einstein Vision.
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Einstein Vision
Harness the power of image recognition to solve an array of use-cases using pre-trained classifiers or custom models.
Einstein Object Detection
Einstein custom object detection enables developers to train models to recognize and count multiple distinct objects within an image, providing granular details like the size and location of each object.
Learn MoreEinstein Image Classification
Einstein custom image classifier enables developers to train deep learning models to recognize and classify images at scale. Developers can bring the power of image recognition to CRM and third-party applications so that end users across sales, service, and marketing can discover new insights about their customers and predict outcomes that lead to smarter decisions.
Learn MoreGeneral Classifier
This model can classify thousands of unique objects found in a given image. The general image classifier performs single label classification on an image. So it’s best used when detecting the presence of at least one of the objects it’s trained to recognize. To see all the individual object it can identify click the class list below.
Learn MoreScene Classifier
The scene classifier can identify 74 different indoor and outdoor scenes. Among many things, this model is useful for finding correlations between your product and the environment it’s often found in. To see all the individual scenes it can identify click the class list below.
Learn MoreFood Classifier
Identify over 500 different foods with this pre-made food classifier. Common use cases revolve around diet or cooking applications. To see all the individual foods it can identify click the class list below.
Learn MoreMulti-Label Classifier
This multi-label model can classify 102 unique items. However, unlike the general classifier, the multi-label classifier can detect the presence of multiple objects it’s trained to identify. To see all the objects it can identify click the class list below.
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