Abstract: A method and apparatus is provided for generating and applying a deep learning model based on a deep learning framework, and relates to the field of computers. A specific implementation solution includes that a basic operating environment is established on a target device, where the basic operating environment is used for providing environment preparation for an overall generation process of a deep learning model; a basic function of the deep learning model is generated in the basic operating environment according to at least one of a service requirement and a hardware requirement, to obtain a first processing result; an extended function of the deep learning model is generated in the basic operating environment based on the first processing result, to obtain a second processing result; and a preset test script is used to perform function test on the second processing result, to output a test result.

Abstract: The present disclosure provides a method and apparatus for recognizing a text, a device and a storage medium, and relates to the field of deep learning technology. A specific implementation comprises: receiving a target image; performing a text detection on the target image using a pre-trained lightweight text detection network, to obtain a text detection box; and recognizing a text in the text detection box using a pre-trained lightweight text recognition network, to obtain a text recognition result.

Abstract: The present disclosure provides a method and apparatus of processing an image, a device and a medium, which relates to a field of artificial intelligence, and in particular to a field of deep learning and image processing. The method includes: determining a background image of the image, wherein the background image describes a background relative to characters in the image; determining a property of characters corresponding to a selected character section of the image; replacing the selected character section with a corresponding section in the background image, so as to obtain an adjusted image; and combining acquired target characters with the adjusted image based on the property.

Abstract: A method and system for command queuing in disk drives may improve performance by queuing multiple commands and sequentially executing them automatically without firmware intervention. The method may use a number of queues, e.g., a staging queue for commands to be executed, an execution queue for commands currently being executed, and a holding queue for commands which have been executed but have not received a status report from a host. With the pipelined nature of queued commands, when data requested by one command are being sent to the host, the queue logic may already be fetching data for the next command. If an error occurs in the transmission, commands in the queues may backtrack and restart from the point where data were last known to have been successfully sent to the host.

Abstract: A method and system for command queuing in disk drives may improve performance by queuing multiple commands and sequentially executing them automatically without firmware intervention. The method may use a number of queues, e.g., a staging queue for commands to be executed, an execution queue for commands currently being executed, or a holding queue for commands which may have been executed but have not received a status report from a host.

Abstract: A method and system for command queuing in disk drives may improve performance by queuing multiple commands and sequentially executing them automatically without firmware intervention. The method may use a number of queues, e.g., a staging queue for commands to be executed, an execution queue for commands currently being executed, and a holding queue for commands which have been executed but have not received a status report from a host. With the pipelined nature of queued commands, when data requested by one command are being sent to the host, the queue logic may already be fetching data for the next command. If an error occurs in the transmission, commands in the queues may backtrack and restart from the point where data were last known to have been successfully sent to the host.