Abstract: An image detection and classification method for faster detection and classification by a neural network is applied to a neural network model including n operators. The neural network model is mapped to a singly-linked list. A vector of data pairs corresponding to the singly-linked list is established, and operator subsets of the neural network model are determined according to the vector. A target image is input to the neural network model, and input data and output data of the operator subset are recorded. The neural network model is applied to detect the target image, and a detection result is output according to the input data and the output data. The method can detect images quickly.

Abstract: A method for identifying images of any kind of object, applied in an electronic device, obtains images to be classified, and obtains classification models and target models. Image segmentation on the images is carried out and target images are obtained; target images are input into each of the input layers and target feature maps are generated; target values according to size of each target feature map are generated; a convolution operation on the target feature maps generates feature values and a target vector of each target model is generated according to average pooling layers, feature values, and target values. The target vector is input into classification layers of a target model and label categories and probability values of categories obtained, enabling classification of images according to the label categories and the probability values of categories.

Abstract: An image classification method applied to an electronic device is provided. The method includes obtaining prediction results of a plurality of test images. A plurality of target images is determined according to the prediction results, and a prediction accuracy rate of a first classification model is calculated based on the plurality of target images. A second classification model is obtained according to the plurality of target images. An initial feature matrix is obtained using the second classification model. An input feature matrix is obtained by performing a dimension raising process on an initial feature matrix. Once a target weight matrix is obtained based on an initial weight matrix, and a target vector is generated according to the target weight matrix and the input feature matrix, a classification result of an image to be classified is obtained by inputting the target vector into a classification layer.

Abstract: An image recognition method applied to an electronic device is provided. The method includes obtaining a recognition region and a plurality of test regions. A plurality of first prediction results is obtained by predicting each of the plurality of test regions using a first recognition model. A prediction accuracy rate is calculated. A plurality of target regions is obtained from the plurality of test regions, and a second recognition model is obtained by adjusting the first recognition model based on the prediction accuracy rate and the plurality of target regions. An initial feature matrix is obtained by inputting the recognition region in the second recognition model. A target vector is generated according to a target feature matrix and an initial weight matrix; and a recognition result of the image to be recognized is obtained by inputting the target vector into the second recognition model.

Abstract: A method for identifying images of any kind of object, applied in an electronic device, obtains images to be classified, and obtains classification models and target models. Image segmentation on the images is carried out and target images are obtained; target images are input into each of the input layers and target feature maps are generated; target values according to size of each target feature map are generated; a convolution operation on the target feature maps generates feature values and a target vector of each target model is generated according to average pooling layers, feature values, and target values. The target vector is input into classification layers of a target model and label categories and probability values of categories obtained, enabling classification of images according to the label categories and the probability values of categories.

Abstract: A method for detecting image sizes using a computer device is provided. In the method, the computer device obtains a pre-trained image processing model and an input image, establishes a stack according to a number of network layers of the pre-trained image processing model. Output feature maps are obtained by processing the input image using the pre-trained image processing model. Image sizes of the output feature maps are detected. The image sizes are written into the stack in sequence, and the network layers deleted iteratively in an order from large to small according to an execution sequence of the network layers of the pre-trained image processing model until the stack is full. By performing the method, output sizes of the input image can be obtained, and a detection efficiency of image sizes can be improved.

Abstract: A method for segmenting a neural network, implemented in an electronic device, includes assigning a plurality of operator groups to a plurality of execution units based on a plurality of segmentation methods; determining operation times of the plurality of execution units and discrete degree of the operation times of the plurality of execution units in each of the plurality of segmentation methods; and segmenting the neural network according to the segmentation method with a lowest discrete degree.

Abstract: An image detection and classification method for faster detection and classification by a neural network is applied to a neural network model including n operators. The neural network model is mapped to a singly-linked list. A vector of data pairs corresponding to the singly-linked list is established, and operator subsets of the neural network model are determined according to the vector. A target image is input to the neural network model, and input data and output data of the operator subset are recorded. The neural network model is applied to detect the target image, and a detection result is output according to the input data and the output data. The method can detect images quickly.

Abstract: An image detection method obtains a neural network model including n operators. End operators are determined from the n operators. An image is input into the neural network model. Whether to delete operation results of the n operators is determined according to the n operators and the end operators when the neural network model processes the image using the n operators. A detection result of the image is output according to the operation results. The method can improve an efficiency of image detection.

Abstract: A method, an apparatus, and a storage medium for dividing a neural network into regions for preventing data duplication and data loss in parallel movements of data between nodes. The method includes obtaining a neural network model comprising n operators; scanning all operator groups in the neural network model; dividing the neural network model into m regions; rescanning all operator groups in the neural network model and identifying broken operator group(s); analyzing input and output of each of the n number of operators in the broken operator group(s) and identifies operators of a specific sort; and adjusting the operators of the specific type to rearranged to keep individual inputs and outputs within a single region.

Abstract: A method of processing and storing general data by means of hardware obtains initial unhashed data and a fixed value. If a value of the initial unhashed data is greater than the fixed value, the initial data is divided into N sub-data or segments. A size of each sub-data is not more than the fixed value, N being an integer greater than 1. The collection of sub sets of data is input into a memory of the electronic device after hashing.

Abstract: A method for extracting an image feature includes obtaining an image, converting the image into an image matrix, dividing the image matrix to obtain an image vector, and dividing the image vector to obtain a first number of image sub blocks; obtaining and dividing a weight matrix to obtain a second number of weight vectors, dividing each weight vector to obtain a first number of weight sub blocks; obtaining and dividing an offset matrix to obtain a second number of offset quantum blocks; and performing vector multiplication on the first number of image sub blocks and the first number of weight sub blocks, adding a second number of feature sub blocks and the second number of offset quantum blocks to obtain image feature sub blocks, forming the image feature from the image feature sub blocks. An electronic device and a non-volatile storage medium performing the above-described method are also disclosed.

Abstract: A processing method for artificial intelligence learning includes generates a directed graph including a number of first nodes according to a model. The model includes a number of tasks. The tasks represented by the first nodes are to be executed by a central processing unit (CPU) and a graphics processing unit (GPU). The method determines one or more second node sub-graphs formed by one or more second nodes of the first nodes which represent one or more tasks for execution by the GPU, and determines one or more directed acyclic sub-graphs in the one or more second node sub-graphs. The method merges into a single task at least two tasks represented by at least two second nodes in each directed acyclic sub-graph formed by at least two second nodes. A related electronic device and non-transitory storage medium are also provided.

Abstract: A method of processing and storing general data by means of hardware obtains initial unhashed data and a fixed value. If a value of the initial unhashed data is greater than the fixed value, the initial data is divided into N sub-data or segments. A size of each sub-data is not more than the fixed value, N being an integer greater than 1. The collection of sub sets of data is input into a memory of the electronic device after hashing.

Abstract: A test method based on improved REST protocols and an electronic device applying the method are disclosed. In response to a received test task, the method sends a REST control request in JSON format from the client to the server and receives a REST response fed back by the server according to the REST control request. A status code is obtained from the REST response and response mode is determined according to the status code, thereby testing the client and the server based on the improved REST protocol. The REST protocol is easy to upgrade, supports multiple protocols, and reduces the complexity of the system, the data is simple and flexible.

Abstract: A projection display apparatus and digital zooming method thereof is disclosed. The projection display apparatus receives a first image and includes a control unit, an image processor, an image scaler, a projection lens and a display unit. The control unit is for calculating an image area needed to be removed from the first image according to a zooming multiple. The image processor is for removing the image area form the first image to generate a second image. The image scaler is for scaling the second image to generate a third image. The display unit is for receiving the third image and projecting the third image onto a screen via the projection lens. The third image has a specific pixel count relative to the display unit.

Abstract: A method for managing bulb brightness, which is suitable for use in a displaying apparatus based on lamp, is proposed. The characteristics of the method include switching the lamp to the standard mode with maximum brightness in the displaying state, and switching the lamp to the ECO mode with reduced brightness in the other states. Therefore, this method is more adaptive than the prior art, making the apparatus to be more convenient in use, and optimizing the use of the lamp in presenting higher picture quality, and prolonging the lifetime of lamp.

Abstract: A projector comprising a main body, a radio frequency identification (RFID) tag and a controller is provided. The main body comprises a light module and an optical engine. The light module is used for emitting a light. The optical engine is used for receiving the light and outputting a projecting image. The RFID tag is coupled to the main body and is used for providing an identification data to recognize the identity of the projector or a user. The controller reads the identification data from the radio frequency identification tag or writes the identification data to the radio frequency identification tag by a communication interface.

Abstract: A projection display system and cursor control method thereof is disclosed. The projection display system includes a projection display device and an operation unit. The projection display device is for displaying an image and a cursor. The projection display device includes an on screen display (OSD) unit for generating the cursor in the image. The operation unit is for controlling a moving direction of the cursor through the OSD unit.

Abstract: A projection display apparatus and digital zooming method thereof is disclosed. The projection display apparatus receives a first image and includes a control unit, an image processor, an image scaler, a projection lens and a display unit. The control unit is for calculating an image area needed to be removed from the first image according to a zooming multiple. The image processor is for removing the image area form the first image to generate a second image. The image scaler is for scaling the second image to generate a third image. The display unit is for receiving the third image and projecting the third image onto a screen via the projection lens. The third image has a specific pixel count relative to the display unit.