Parcel identification method and system based on pruning lightweight model
A lightweight model and recognition method technology, applied in the field of image recognition, can solve the problems of high time and memory consumption, high computational complexity of neural network models, and difficult deployment, etc., to achieve low memory usage, realize application value, and good stability Effect
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Embodiment 1
[0046] A specific embodiment of the present invention discloses a package identification method for pruning a lightweight model. like figure 1 As shown, the method includes the following steps:
[0047] S101: Input the training image into the pre-trained neural network model to be pruned, and extract the feature map matrix of each convolutional layer;
[0048] It should be noted that this embodiment does not limit the specific network structure of the neural network model to be pruned, as long as it is a neural network model composed of convolutional layers having a CONV-BN-ReLU structure. The CONV-BN-ReLU structure is widely used in a variety of mainstream convolutional neural network models, so that the pruning scheme in this method can be easily applied to mainstream neural network models in the fields of classification and recognition, and realizes lightweight neural network models. .
[0049] Exemplarily, the neural network model to be pruned may be a neural network m...
Embodiment 2
[0110] Another embodiment of the present invention discloses a package identification system based on a pruning lightweight model, thereby realizing the package identification method in the first embodiment. For the specific implementation of each module, refer to the corresponding description in the first embodiment. like image 3 As shown, the system includes:
[0111] The feature map matrix extraction module S201 is used to input the training picture into the pre-trained neural network model to be pruned, and extract the feature map matrix of each convolution layer;
[0112] The von Neumann graph entropy calculation module S202 is used to convert the feature map matrix of each convolution layer into a weighted undirected graph, construct an improved Laplacian matrix according to the magnitude matrix of the feature map matrix, and calculate The von Neumann graph entropy is taken as the original value of each convolutional layer; the single vertex in the weighted undirected...
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