Abstract: Provided is a method and device for detecting defect, a computer readable storage medium and an electronic device, the method including: acquiring (S310) a detection task, and acquiring various types of images corresponding to the detection task; acquiring (S320) defect detection models trained by a same initial model corresponding to the types of the images respectively; and obtaining (S330) defect detection results by performing defect detection on respective type of images using the defect detection model corresponding to the type of the images.

Abstract: The present disclosure relates to the technical field of metal foils. Disclosed is a metal foil, including a conductive layer and a bearing layer, the conductive layer and the bearing layer being stacked. The conductive layer is configured to manufacture a conductive line. When a circuit board is manufactured using the metal foil, the bearing layer is separated from the conductive layer by a first etching solution, and a roughness Rz of a surface, close to the conductive layer, of the bearing layer is less than or equal to 2 microns. When the circuit board is manufactured using the metal foil, a surface of the conductive line is substantially flush with a surface of a substrate after the bearing layer is removed, and the surface roughness of the conductive line is low, so that the product requirement for high dimensional accuracy may be satisfied. Meanwhile, embodiments of the present disclosure further correspondingly provide a circuit board and a method for manufacturing the circuit board.

Abstract: An image marking method, apparatus and system, which relates to the technical field of image processing. The present disclosure includes, when the working mode of the first client is a first mode, receiving a first marking task assigned by a second client, on the condition that the image marking approach is the first marking approach, according to a neural network model, determining a first marking result corresponding to the first original image; on the condition that the image marking approach is the second marking approach, according to an unsupervised algorithm model, determining a second marking result corresponding to the first original image; on the condition that the image marking approach is the third marking approach, receiving a third marking result inputted by a user into the first original image; and sending a target marking result to the second client.

Abstract: A method and a device for processing product manufacturing messages, and an electronic device are disclosed. The method for processing product manufacturing messages includes: monitoring a plurality of product manufacturing messages; establishing a product defect analysis task queue based on the plurality of product manufacturing messages; distributing product defect analysis tasks to product manufacturing assisting devices based on the product defect analysis task queue, wherein the product defect analysis tasks include a task of identifying product defect content based on a defect identification model; wherein the product defect content includes any one or more of: product defect type, product defect location, and product defect size.

Abstract: The disclosure discloses a metal foil, a metal foil with a carrier, a copper-clad laminate and a Printed Circuit Board. Herein, a plurality of protrusions are distributed on one face of the metal foil, and the protrusions have the follow microscopic morphology: the lower half part of the protrusion connected with the one face of the metal foil is provided with a limiting part, and the diameter of a circumscribed circle of the cross section of the limiting part is smaller than the skin depth of the metal foil; and the surface area of the part of the protrusion above the limiting part is larger than the surface area of other parts of the protrusion.

Abstract: A method and a device for processing product manufacturing messages, and an electronic device are disclosed. The method for processing product manufacturing messages includes: monitoring a plurality of product manufacturing messages; establishing a product defect analysis task queue based on the plurality of product manufacturing messages; distributing product defect analysis tasks to product manufacturing assisting devices based on the product defect analysis task queue, wherein the product defect analysis tasks include a task of identifying product defect content based on a defect identification model; wherein the product defect content includes any one or more of: product defect type, product defect location, and product defect size.

Abstract: Provided is a method and device for detecting defect, a computer readable storage medium and an electronic device, the method including: acquiring (S310) a detection task, and acquiring various types of images corresponding to the detection task; acquiring (S320) defect detection models trained by a same initial model corresponding to the types of the images respectively; and obtaining (S330) defect detection results by performing defect detection on respective type of images using the defect detection model corresponding to the type of the images.

Abstract: The present disclosure relates to a task processing method and device based on defect detection, a computer readable storage medium, and a task processing apparatus. The method includes receiving a detection task; determining a task type of the detection task; storing the detection task in a task queue if the task type is a target task type; and executing the detection task in a preset order and generating a feedback signal when a processor is idle. The detection task of the target task type includes an inference task and a training task. Executing the training task includes modifying configuration information according to a preset rule based on product information in the detection task; acquiring training data and an initial model according to the product information; and using the training data to train the initial model according to the configuration information to obtain a target model and store it in memory.

Abstract: A distributed computing system for product defect analysis is disclosed. The distributed computing system for product defect analysis includes a computing cluster for processing product manufacturing messages, a computing cluster for identifying product defect, a product image database, and a client device.

Abstract: A method and device for detecting a defect and method for training a model are provided. The method for detecting the defect includes: acquiring a sample data set and identifying feature information of the sample data set; acquiring an initial model; configuring a training parameter based on the feature information; obtaining a target model by training, according to the training parameter, the initial model with the sample data set; and obtaining defect information of a product by inputting real data of the product into the target model. The training parameter includes at least one of a learning rate descent strategy, a total number of training rounds and a test strategy, the learning rate descent strategy includes a number of learning rate descents and a round number when a learning rate descends, and the test strategy includes a number of tests and a round number when testing.

Abstract: A distributed computing system for product defect analysis is disclosed. The distributed computing system for product defect analysis includes a computing cluster for processing product manufacturing messages, a computing cluster for identifying product defect, a product image database, and a client device.

Abstract: A method and a device for processing product manufacturing messages, and an electronic device are disclosed. The method for processing product manufacturing messages includes: monitoring a plurality of product manufacturing messages; establishing a product defect analysis task queue based on the plurality of product manufacturing messages; distributing product defect analysis tasks to product manufacturing assisting devices based on the product defect analysis task queue, wherein the product defect analysis tasks include a task of identifying product defect content based on a defect identification model; wherein the product defect content includes any one or more of: product defect type, product defect location, and product defect size.

Abstract: The present disclosure provides a method and device for detecting an image category. The method includes: acquiring a sample data set including a plurality of sample images labeled with a category, the sample data set including a training data set and a verification data set; training a deep learning model using the training data set to obtain, according to different numbers of training rounds, at least two trained models; testing the at least two trained models using the verification data set to generate a verification test result; generating, based on the verification test result, a verification test index; determining, according to the verification test index, a target model from the at least two trained models; and predict a to-be-tested image of the target object using the target model to obtain the category of the to-be-tested image.

Abstract: The present disclosure relates to a task processing method and device based on defect detection, a computer readable storage medium, and a task processing apparatus . The method includes receiving a detection task; determining a task type of the detection task; storing the detection task in a task queue if the task type is a target task type; and executing the detection task in a preset order and generating a feedback signal when a processor is idle. The detection task of the target task type includes an inference task and a training task. Executing the training task includes modifying configuration information according to a preset rule based on product information in the detection task; acquiring training data and an initial model according to the product information; and using the training data to train the initial model according to the configuration information to obtain a target model and store it in memory.

Abstract: Provided are an anti-CLDN18.2 antibody or an antigen-binding fragment thereof, a derivative comprising said antibody or antigen-binding fragment thereof, a pharmaceutical composition, and related use of said antibody or antigen-binding fragment thereof for treating, diagnosing and detecting cancers.

Abstract: An image marking method, apparatus and system, which relates to the technical field of image processing. The present disclosure includes, when the working mode of the first client is a first mode, receiving a first marking task assigned by a second client, on the condition that the image marking approach is the first marking approach, according to a neural network model, determining a first marking result corresponding to the first original image; on the condition that the image marking approach is the second marking approach, according to an unsupervised algorithm model, determining a second marking result corresponding to the first original image; on the condition that the image marking approach is the third marking approach, receiving a third marking result inputted by a user into the first original image; and sending a target marking result to the second client.