Method and device for searching coordinate recognition model based on NAS technology

A coordinate recognition and baseline model technology, applied in the field of computer vision, can solve the problems of reducing the accuracy and speed of neural network models, lack of targeted optimization of human action categories, etc., and achieve the effects of reducing performance requirements, saving time, and improving accuracy and speed

Pending Publication Date: 2021-05-28
SHANGHAI YITU NETWORK SCI & TECH
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However, due to the lack of targeted optimization of the human action category by manually designing th

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  • Method and device for searching coordinate recognition model based on NAS technology
  • Method and device for searching coordinate recognition model based on NAS technology
  • Method and device for searching coordinate recognition model based on NAS technology

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[0057]Next, the technical solutions in the present application embodiment will be clearly described in the present application embodiment, and it is clear that the described embodiments are merely the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative labor premises, all of the present application protected.

[0058]At present, humanistic attitude is estimated to be one of the most challenging research directions in the field of computer visual.

[0059]In the relevant technique, when the human body included in the image is estimated, it is generally an estimate of the human body to be estimated to estimate the original image to the training-trained neural network model, which is estimated to estimate the posture of the human body by manually designing and training. , Based on the mainstream network structure in the neural networ...

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Abstract

The invention relates to the technical field of computer vision, and relates to a method and device for searching a coordinate recognition model based on an NAS technology, in particular to a method and device for training the coordinate recognition model, and the method comprises the steps: obtaining the value range of each hyper-parameter of a baseline model, and obtaining the value range of each hyper-parameter according to the value range of each hyper-parameter; combining the hyper-parameters under different values to generate a plurality of numerical value combinations; for each numerical value combination, setting each hyper-parameter of the baseline model as each value in any numerical value combination, and obtaining a candidate coordinate identification model under the numerical value combination; for each candidate coordinate recognition model, inputting the image sample set into any candidate coordinate recognition model for training, and calculating an error value of the candidate coordinate recognition model; and taking the candidate coordinate recognition model meeting the preset error value condition as a final optimized coordinate recognition model, so that automatic design of the coordinate recognition model can be realized in combination with NAS.

Description

technical field [0001] The present application relates to the technical field of computer vision, and in particular to a search method and device for a coordinate recognition model based on NAS technology. The present application specifically provides a method and device for training a coordinate recognition model. Background technique [0002] At present, when the action category of the human body contained in the image to be recognized is recognized, it is generally realized through a neural network model manually designed and trained in advance. However, due to the lack of targeted optimization of the human action category through the manual design of the model, the accuracy and speed of the neural network model in recognizing human actions will be reduced. [0003] In order to solve the above problems, in the related art, the network structure contained in the neural network model can be automatically designed through neural network search (Neural Architecture Search, N...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/08
CPCG06N3/08G06V40/10G06V40/20G06F18/241
Inventor 王蔚田晓玮聂学成
Owner SHANGHAI YITU NETWORK SCI & TECH
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