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Data model training method and device, equipment and medium

A technology of data model and training method, applied in biological neural network model, character and pattern recognition, instrument, etc., can solve the problem of low model training efficiency, avoid greatly increasing training complexity, improve training effect, and improve summary ability Effect

Pending Publication Date: 2021-12-07
PINGAN INT SMART CITY TECH CO LTD
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Problems solved by technology

[0004] The main purpose of this application is to provide a data model training method, device, medium and equipment, aiming to solve the technical problem of low training efficiency of the model in the prior art

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  • Data model training method and device, equipment and medium
  • Data model training method and device, equipment and medium
  • Data model training method and device, equipment and medium

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Embodiment Construction

[0050] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0051] refer to figure 1 , in order to achieve the above-mentioned purpose of the invention, the present application proposes a data model training method, the method comprising:

[0052] S1: Obtain an initial picture set, where the initial picture set includes several initial picture samples;

[0053] S2: Identify the target object in each of the initial image samples, and mark the target object respectively to obtain the target contour;

[0054] S3: Establish a virtual coordinate system in each of the initial picture samples, calculate the picture midpoint of the initial...

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Abstract

The invention relates to the technical field of artificial intelligence, and discloses a data model training method and device, a medium and equipment, and the method comprises the steps: obtaining an initial picture set; identifying and respectively carrying out contour marking on the target object; establishing a virtual coordinate system in each initial picture sample, and carrying out coordinate alignment on the contour center of the target contour and the middle point of the picture; taking the initial picture sample after coordinate alignment as a first training sample, and training based on the first training sample to obtain a first data model; taking the first training samples of which the confidence coefficient parameters are greater than a preset confidence threshold as satisfactory samples, taking the first training samples of which the confidence coefficient parameters are not greater than the confidence threshold as unsatisfactory samples, and removing the unsatisfactory samples; obtaining a supplementary training sample, and obtaining a second training sample according to the supplementary training sample and the satisfactory sample; training the first data model through a second training sample to obtain a second data model; therefore, the training efficiency of the model is improved.

Description

technical field [0001] This application relates to the technical field of artificial intelligence, in particular to a data model training method, device, equipment and medium. Background technique [0002] In the deep learning training samples based on yolov3, there may be some training samples that are too inferior, such as smearing, occlusion, unclear and other problems, or the lack of training samples due to the scarcity of samples in some scenes, which will lead to the training of the model. The accuracy is not high, and the incorporation of inferior samples will also lead to excessive complexity during training. [0003] In view of the low quality of some training samples, the traditional method is to manually review all the sample pictures to screen out unqualified samples, and whether the pictures are qualified or not has subjective components; for the problem of sample scarcity, the main method is to manually screen high-quality samples and use keras data enhancement...

Claims

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Application Information

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IPC IPC(8): G06K9/46G06K9/62G06N3/04
CPCG06N3/045G06F18/214
Inventor 黄哲
Owner PINGAN INT SMART CITY TECH CO LTD