Training method of garbage classification model and garbage classification method and device

Abstract

The invention discloses a garbage classification model training method and a garbage classification method and device, and the method comprises the steps: carrying out the resampling of a pre-labeled garbage image sample set, and obtaining a balanced sample set; based on the junk image sample set and the balanced sample set, training a preset initial smooth perception model until the initial smooth perception model reaches a first preset convergence condition, and obtaining a target smooth perception model; performing iterative training on a preset first garbage classification model based on the garbage image sample set to obtain a first loss value, and performing smoothing processing on the first loss value by using the target smoothing perception model to obtain a second loss value; and finally, according to the second loss value, model parameters of the first garbage classification model are updated until the first garbage classification model reaches a second preset convergence condition, a target garbage classification model is obtained, and the accuracy of a classification result of the model is improved.

Description

technical field [0001] The present application relates to the field of robot technology, in particular to a training method for a garbage classification model, a garbage classification method and a device. Background technique [0002] Garbage classification is the core function of intelligent cleaning robots. The deep convolutional neural network is used to reason about the garbage categories in the collected pictures, and according to the reasoning results, devices such as robotic arms are used to clean up different types of garbage. The deep convolutional neural network’s reasoning The ability directly determines the garbage sorting performance of the intelligent cleaning robot. [0003] The training set of the garbage classification model usually contains hundreds of thousands or even millions of training samples, and several annotators are required to label the garbage categories in the training samples. Because the categories of some garbage are confusing, and the cog...

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Problems solved by technology

[0004] This application provides a training method for a garbage classification model, a garbage classification method and

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