Scoring method, device, computer equipment and storage medium
A technology for images to be detected and neural network models, applied in the field of image processing, can solve problems such as large workload, scoring errors, and large differences, and achieve the effect of improving efficiency
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Embodiment 1
[0028] Figure 1a It is a flow chart of a scoring method provided by Embodiment 1 of the present invention. This embodiment is applicable to the situation of evaluating images drawn by users according to standard graphics. This method can be executed by the scoring device provided by the embodiment of the present invention. The device It can be implemented in the form of software and / or hardware, and generally can be integrated into terminal equipment used by judges, such as PCs or tablet computers. Such as Figure 1a As shown, the method of this embodiment specifically includes:
[0029] S110. Acquire at least one image to be detected, where the image to be detected includes graphic information.
[0030] Generally, in the visual test, you can choose to set multiple standard graphics, and the user can draw the same graphics according to the standard graphics, and evaluate the user's visual development age by evaluating the standardization degree of the graphics drawn by the us...
Embodiment 2
[0053] Figure 2a It is a flow chart of a scoring method provided by Embodiment 2 of the present invention. This embodiment is embodied on the basis of the above embodiments. In this embodiment, the pre-trained neural network model is embodied as: at least one Neural network model unit. Specific as Figure 2a As shown, the specific methods include:
[0054] S210. Acquire at least one image to be detected, where the image to be detected includes graphic information.
[0055] S220. Input the at least one image to be detected into at least one neural network model unit, and obtain a target standard deviation score of the at least one image to be detected, and a target topology corresponding to the at least one image to be detected.
[0056] In this embodiment, when the pre-trained neural network model includes at least two neural network model units, in two adjacent neural network model units, the target standard deviation of the output of the previous neural network model uni...
Embodiment 3
[0081] image 3 It is a schematic structural diagram of a scoring device provided in Embodiment 3 of the present invention, such as image 3 As shown, the device specifically includes:
[0082] An image acquisition module 310, configured to acquire at least one image to be detected, where the image to be detected includes graphic information;
[0083] The image evaluation module 320 is configured to input the at least one image to be detected into a pre-trained neural network model to obtain a standard deviation score of the at least one image to be detected.
[0084] The embodiment of the present invention pre-trains the neural network model, inputs the image to be detected into the trained neural network model, and obtains the standard deviation score of the image to be detected relative to the standard graphic, which solves the problem of judging the standard degree of the graphic in the prior art The problem of strong subjectivity and high labor cost of manual scoring ca...
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