公开(公告)号：US20220222550A1

公开(公告)日：2022-07-14

申请号：US17583133

申请日：2022-01-24

Applicant: Apple Inc.

Inventor： Alexander James Oscar Craver KIRCHHOFF ,  Ali FARHADI ,  Anish Jnyaneshwar PRABHU ,  Carlo Eduardo Cabanero DEL MUNDO ,  Daniel Carl TORMOEN ,  Hessam BAGHERINEZHAD ,  Matthew S. WEAVER ,  Maxwell Christian HORTON ,  Mohammad RASTEGARI ,  Robert Stephen KARL, JR. ,  Sophie LEBRECHT

IPC: G06N5/04 ,  G06F11/34 ,  H04N5/232 ,  G06F8/41 ,  G06F8/10

Abstract: In one embodiment, a method includes providing, to a client system of a user, a user interface for display. The user interface may include a first set of options for selecting an artificial intelligence (AI) task for integrating into a user application, a second set of options for selecting one or more devices on which the user wants to deploy the selected AI task, and a third set of options for selecting one or more performance constraints specific to the selected devices. User specifications may be received based on user selections in the first, second, and third sets of options. A custom AI model may be generated based on the user specifications and sent to the client system of the user for integrating into the user application. The custom AI model once integrated may enable the user application to perform the selected AI task on the selected devices.

公开(公告)号：US20240144566A1

公开(公告)日：2024-05-02

申请号：US18544288

申请日：2023-12-18

Applicant: Apple Inc.

Inventor： Hessam BAGHERINEZHAD ,  Maxwell HORTON ,  Mohammad RASTEGARI ,  Ali FARHADI

IPC: G06T11/60 ,  G06F18/214 ,  G06F18/241 ,  G06N3/045 ,  G06N3/08 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G06V20/52 ,  G06V40/10

CPC classification number: G06T11/60 ,  G06F18/2148 ,  G06F18/241 ,  G06N3/045 ,  G06N3/08 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06V20/52 ,  G06V40/10 ,  G06T2210/22 ,  G06V20/68

Abstract: Systems and methods are disclosed for training neural networks using labels for training data that are dynamically refined using neural networks and using these trained neural networks to perform detection and/or classification of one or more objects appearing in an image. Particular embodiments may generate a set of crops of images from a corpus of images, then apply a first neural network to the set of crops to obtain a set of respective outputs. A second neural network may then be trained using the set of crops as training examples. The set of respective outputs may be applied as labels for the set of crops.