Abstract: A method for generating an order includes acquiring by one or more processors, an initial image and a first detection result of a target container before being subjected to a pick-up operation, where the first detection result includes a first detection image with a number of first commodity frames obtained by processing the initial image, and first identification information corresponding to a commodity in each of the first commodity frames, acquiring by one or more processors, a result image of the target container after performing the pick-up operation, generating by one or more processors, an RGB difference image between before-shopping and after-shopping, based on the result image and the initial image, where the RGB difference image is obtained by calculating an absolute value of differences of individual pixels of the initial image and the result image before and after shopping in three RGB channels.

Abstract: The present disclosure discloses a method and apparatus for scheduling a resource for a deep learning framework. The method can comprise: querying statuses of all deep learning job objects from a Kubernetes platform at a predetermined interval; and submitting, in response to finding from the queried deep learning job objects a deep learning job object having a status conforming to a resource request submission status, a resource request to the Kubernetes platform to schedule a physical machine where the Kubernetes platform is located to initiate a deep learning training task. The method can completely automate the allocation and release on the resource of the deep learning training task.

Abstract: The disclosure discloses a method and apparatus for updating a deep learning model. An embodiment of the method comprises: executing following updating: acquiring a training dataset under a preset path, training a preset deep learning model based on the training dataset to obtain a new deep learning model; updating the preset deep learning model to the new deep learning model; increasing training iterations; determining whether a number of training iterations reaches a threshold of training iterations; stopping executing the updating if the number of training iterations reaches the threshold of training iterations; and continuing to execute the updating after an interval of a preset time length if the number of training iterations fails to reach the threshold of training iterations. This embodiment has improved the model updating efficiency.

Abstract: A method, device, apparatus and computer-readable storage medium for mining an enterprise relationship. The method can include acquiring Internet data of multiple types, identifying enterprise names from the Internet data by an enterprise name identification model, performing data mining to the Internet data in a frequent item mining algorithm to obtain relational degrees among the enterprise names, and according to the relational degrees among the enterprise names, extracting the enterprise relationship from the Internet data by an extractor. A deeper and more comprehensive enterprise relationship can be obtained by performing data mining to Internet data of multiple types by using an enterprise name recognition model. By using the frequent items mining algorithm, enterprises among which there are higher relational degrees can further be obtained, so as to obtain a more accurate enterprise relationship.

Abstract: A system comprises: a data warehouse, a storage device and a cluster including a plurality of computing nodes; the data warehouse is configured to store task data obtained from the user; at least one computing node in the cluster includes a resource scheduling component, and is configured to perform resource scheduling for the task and determine a computing node executing the task; the computing node executing the task comprises a model training component and/or a prediction component; the model training component is configured to, according to task data, invoke a corresponding type of learning model from the storage device; use sample data and training target included in the task data to train the learning model, to obtain the prediction model corresponding to the task and store the prediction model in the storage device; the prediction component is configured to obtain a prediction result output by the prediction model.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for recommending a video. A specific implementation of the method includes: finding a recommended video corresponding to a target video from all candidate videos based on similarities of content characteristics of the videos, the target video being a video to be played on a terminal of a user; and sending play information of the recommended video corresponding to the target video to the terminal of the user. The method finds a video similar on video content to the target video that the user desires to view based on the content characteristic of the video, and recommends the video similar on video content to the target video that the user desires to view to the user.

Abstract: A method and an apparatus for inspecting a corrosion defect of a ladle are provided. The method includes: acquiring images from various angles using an image acquisition apparatus inside a to-be-inspected ladle; and inputting the acquired images into a defect inspection system to obtain a label representing a defect category, the defect inspection system including: a deep convolutional neural network that predicts a category of a corrosion defect of the ladle included in the images based on the input images. This method has the advantages of high safety, high accuracy and high real-time performance in inspecting the ladle status.

Abstract: An integrated facial recognition method and system. The method includes: receiving a request for acquiring an integrated facial recognition service sent by a user terminal, which includes: an identifier of a user-selected model associated with facial recognition of the user, and an identifier of an operation selected by the user from candidate operations; and executing distributedly an operation selected by the user from the candidate operations on the user-selected model associated with the facial recognition of the user to obtain an operation result, and storing the operation result. The embodiment has realized completing the operations such as training a model or developing a facial recognition application, without the need of buying hardware and establishing a software environment by the user, thereby saving the development cost and improving the convenience of using the facial recognition service.

Abstract: A text recognition method, a text recognition apparatus, an electronic device, and a storage medium are provided. The method includes: extracting character prediction regions of a text line image using a constructed text recognition model, to obtain a character region set; calculating horizon-distance IOUs between the character prediction regions in the character region set according to a position of each character prediction region in the character region set; removing overlapped character prediction regions from the character region set, according to the horizon-distance IOUs between the character prediction regions in the character region set, to obtain a character position set; and sorting and outputting character categories corresponding to the character prediction regions in the character position set according to positions of the character prediction regions in the character position set.

Abstract: The present disclosure provides a method, a device, an electronic apparatus, and a storage medium for generating an order, with the method for generating an order including the following steps of: acquiring an initial image and a first detection result of a target container before the occurrence of a pick-up operation, wherein the first detection result includes a first detection image with a plurality of first commodity frames obtained by processing the initial image, and first identification information corresponding to the commodity in each of the first commodity frames; acquiring a result image of the target container after the finish of the pick-up operation; generating an RGB difference image between before-shopping and after-shopping based on the result image and the initial image; and generating a commodity order corresponding to the removed commodity based on the RGB difference image and the first detection result.

Abstract: A text recognition method, a text recognition apparatus, an electronic device, and a storage medium are provided. The method includes: extracting character prediction regions of a text line image using a constructed text recognition model, to obtain a character region set; calculating horizon-distance IOUs between the character prediction regions in the character region set according to a position of each character prediction region in the character region set; removing overlapped character prediction regions from the character region set, according to the horizon-distance IOUs between the character prediction regions in the character region set, to obtain a character position set; and sorting and outputting character categories corresponding to the character prediction regions in the character position set according to positions of the character prediction regions in the character position set.

Abstract: The present disclosure provides a deep learning assignment processing method and apparatus, a device and a storage medium. It is feasible to obtain the deep learning assignment submitted by the user in a predetermined manner, the predetermined manner comprising the web UI manner, then submit the deep learning assignment to the deep learning system so that the deep learning system runs the submitted deep learning assignment. As compared with the prior art, processing such as programming is not needed upon submitting the deep learning assignment in the solutions of the present disclosure, thereby simplifying the user's operations, improving the processing efficiency of the deep learning assignment, and accelerating the user's speed of developing deep learning.

Abstract: A method, apparatus and system for detecting structural variations is provided.

Abstract: The application provides a method and a device for detecting a defect in a steel plate, as well as an apparatus and a server therefor. The method for detecting a defect in a steel plate comprises: receiving image data of the steel plate, and generating a defect detection request according to the image data; monitoring computing loads of a plurality of servers, and sending the image data and the defect detection request to a first server; receiving, from the first server, a detection result obtained by calculating the image data using the detection model; and operating according to the detection result, wherein the detection result comprises a selected one of a pass result and a defect result. With the proposed method, the position and the classification of the at least one defect can be obtained, so that the detection accuracy is improved.

Abstract: A method, device, apparatus and computer-readable storage medium for mining an enterprise relationship. The method can include acquiring Internet data of multiple types, identifying enterprise names from the Internet data by an enterprise name identification model, performing data mining to the Internet data in a frequent item mining algorithm to obtain relational degrees among the enterprise names, and according to the relational degrees among the enterprise names, extracting the enterprise relationship from the Internet data by an extractor. A deeper and more comprehensive enterprise relationship can be obtained by performing data mining to Internet data of multiple types by using an enterprise name recognition model. By using the frequent items mining algorithm, enterprises among which there are higher relational degrees can further be obtained, so as to obtain a more accurate enterprise relationship.

Abstract: A method and an apparatus for inspecting a corrosion defect of a ladle are provided. The method includes: acquiring images from various angles using an image acquisition apparatus inside a to-be-inspected ladle; and inputting the acquired images into a defect inspection system to obtain a label representing a defect category, the defect inspection system including: a deep convolutional neural network that predicts a category of a corrosion defect of the ladle included in the images based on the input images. This method has the advantages of high safety, high accuracy and high real-time performance in inspecting the ladle status.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for recommending a video. A specific implementation of the method includes: finding a recommended video corresponding to a target video from all candidate videos based on similarities of content characteristics of the videos, the target video being a video to be played on a terminal of a user; and sending play information of the recommended video corresponding to the target video to the terminal of the user. The method finds a video similar on video content to the target video that the user desires to view based on the content characteristic of the video, and recommends the video similar on video content to the target video that the user desires to view to the user.

Abstract: The present disclosure discloses a method and apparatus for scheduling a resource for a deep learning framework. The method can comprise: querying statuses of all deep learning job objects from a Kubernetes platform at a predetermined interval; and submitting, in response to finding from the queried deep learning job objects a deep learning job object having a status conforming to a resource request submission status, a resource request to the Kubernetes platform to schedule a physical machine where the Kubernetes platform is located to initiate a deep learning training task. The method can completely automate the allocation and release on the resource of the deep learning training task.

Abstract: The application provides a method and a device for detecting a defect in a steel plate, as well as an apparatus and a server therefor. The method for detecting a defect in a steel plate comprises: receiving image data of the steel plate, and generating a defect detection request according to the image data; monitoring computing loads of a plurality of servers, and sending the image data and the defect detection request to a first server; receiving, from the first server, a detection result obtained by calculating the image data using the detection model; and operating according to the detection result, wherein the detection result comprises a selected one of a pass result and a defect result. With the proposed method, the position and the classification of the at least one defect can be obtained, so that the detection accuracy is improved.

Abstract: This disclosure describes a user interface (UI) conceptual model and a data model for quickly and easily developing and managing a GUI. The present methods and systems described herein provide for defining one or more UI elements in the UI conceptual model. The one or more UI elements may further reference one or more model objects in the data model. The one or more model objects may include one or more tasks, which may each be mapped to one or more commands for performing each task, or one or more data items, which may each be mapped to rules or conditions for transforming data. According to embodiments, graphics or functionality of a GUI may be easily and efficiently modified by adding, deleting, or modifying UI elements in the UI conceptual model or by adding, deleting, or modifying referenced model objects in the data model, thereby reducing management and development costs.