The invention provides a TensorFlow-based deep learning image classification and application deployment method, which comprises the following steps: 1) constructing a Tensorflow machine learning development environment; 2) data acquisition and conversion: obtaining a lot of image data having tags or having no tags form an internet through a distributed crawler system, and carrying out preprocessing on the image data; 3) model establishing and training: establishing a classification model, carrying out training, test and verification on the classification model according to the obtained image data, and keeping the trained classification model; 4) exporting the trained classification model; 5) defining a server interface, and creating a classification server for the exported classification model; 6) creating a request service and establishing connection for a mobile terminal and the classification server, creating a Web APP, the mobile terminal uploading the image data through the Web APP, and calling the classification model to realize automatic image classification; and 7) product deployment, and applying the classification server to products.

TensorFlow-based deep learning image classification and application deployment method

The embodiment of the invention discloses an optimization method and device of a convolutional neural network (CNN) and a computer storage medium. The method includes: constructing the convolutional neural network, wherein the convolutional neural network includes at least four network layers of an image input layer, at least one convolutional layer, at least one pooling layer and at least one fully connected layer; decreasing the number of convolution kernels in the CNN when the number of to-be-detected objects is less than a preset threshold value; dividing an input image of the image inputlayer into at least one memory data segment, which is stored by utilizing continuous memory, according to a set border determination strategy, and utilizing a set continuous memory copy function to carry out data copying on each memory data segment; and merging original parameters in a batch normalization (BN) layer and parameters of the convolutional layer or the fully connected layer according to the set merging strategy, and using merged parameters as new parameters of the batch normalization layer, wherein the batch normalization layer is after the convolutional layer or the fully connected layer. Calculation consumption in carrying out detection through the CNN is reduced.

Optimization method and device of convolutional neural network (CNN) and computer storage medium

The invention discloses a method based on D. MobileNet (Dilated-Mobilenet) neural network image classification method. By combining hole convolution with MobileNet and improving the convolution kernelreceptive field of high-resolution input layer, the quality of output features is improved, and the number of network parameters is not increased, so that the network structure can improve classification accuracy. The method comprises the following steps: 1) preparing a data set; 2) building a MobileNet network; 3) building D-MobileNet network; 4) setting up super parameter. After the model is trained, the pictures of the verification set are verified by the trained convolution neural network model, and classification prediction is complete. The experimental result shows that the invention can obtain better classification accuracy than the MobileNet network.

D-MobileNet-based neural network image classification method

The invention relates to the technical field of article classification and monocular image depth estimation in the field of computer vision, and proposes a model which enhances the classification performance through introduction of depth information and only needs an RGB image instead of a real depth image acquired by a sensor as input. The invention provides an article classification method basedon depth recovery information. The method includes following steps: (1) preprocessing a data set; (2) establishing a depth recovery model in the model; (3) training two image classification models which respectively receive RGB and depth images as input; (4) establishing a final fusion model, and performing training and tests; (5) migrating a trained fusion network in step 4 to a classified dataset of a natural image; and (6) comparing effects of image classification of the model on two disclosed data sets and performing visualization. The method is mainly applied to occasions of article classification and monocular image estimation in the field of computer vision.

Article classification method based on depth recovery information

Embodiments of the invention provide an image classification method and device, electronic equipment and a storage medium. The image classification method comprises the following steps of: obtaining ato-be-classified image; inputting the to-be-classified image into a pre-trained convolutional neural network model so as to a confidence coefficient indicating that the to-be-classified image belongsto each category; and determining a category of the to-be-classified image according to a size relationship between a preset confidence coefficient threshold value of each category and the confidencecoefficient indicating that the to-be-classified image belongs to the category, wherein the preset confidence coefficient threshold value of each category ensures that: for each category, a sample obtained through predicting samples in a predetermined threshold value adjustment sample set by utilizing the convolutional neural network model is the confidence coefficient of the category, and when the category of the sample, the confidence coefficient of which is greater than the preset confidence coefficient threshold value, is predicted as the category, a recall rate of the category is greaterthan a preset recall rate and/or accuracy of the category is greater than preset accuracy. Through the method, the recall factor for various categories of images can be improved.

Image classification method and device, electronic equipment and storage medium

The invention discloses an image classification method based on a convolution neural network. The method comprises the following steps: constructing a deep convolution neural network; improving the deep convolution neural network; training and testing the deep convolution neural network; and optimizing the network parameter. By using the image classification method disclosed by the invention, the improvement and the optimization are respectively performed on the network structure and multiple parameters of the convolution neural network, the recognition rate of the deep convolution neural network can be effectively improved, and the accuracy of the image classification is improved.

Image classification method based on convolution neural network

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Image classification method based on convolution neural network