Model optimization method and device based on scene adaptation
A model and scene technology, applied in the field of data analysis, can solve problems such as difficult selection, achieve high accuracy, high adaptability, and ensure the effects of various indicators and generalization capabilities
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Embodiment 1
[0057] figure 1 It is a schematic flowchart of a scene adaptation-based model tuning method in an embodiment of the present invention. Such as figure 1 As shown, the embodiment of the present invention provides a model tuning method based on scene adaptation, the method includes:
[0058] Step 110: Based on the specific scene information provided by the user, determine N algorithm models corresponding to the set scene; wherein, N is a positive integer.
[0059] Specifically, through the model tuning method of the embodiment of the present invention, the user provides specific scene information in the model tuning system, for example, input scenes such as face recognition, defect search, etc., according to the specific scene information and algorithm Matching, according to prior knowledge, there are multiple algorithms for various scenarios, and multiple algorithm models are matched with the adaptation algorithm set for the specific scenario according to the characteristics o...
Embodiment 2
[0077] Based on the same inventive concept as the scene adaptation-based model tuning method in the foregoing embodiments, the present invention provides a scene adaptation-based model tuning device, such as figure 2 As shown, the device includes:
[0078] A first determination unit 11, the first determination unit 11 is configured to determine N algorithm models corresponding to the set scene based on the specific scene information provided by the user; wherein, N is a positive integer;
[0079] A first obtaining unit 12, the first obtaining unit 12 is configured to divide the data set provided by the user to obtain a training set and a test set;
[0080] A second obtaining unit 13, the second obtaining unit 13 is configured to respectively train each algorithm model through the training set, and obtain one or more sub-models corresponding to each algorithm model;
[0081] The second determination unit 14 is configured to respectively test one or more sub-models correspondi...
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