A device for determining a CNN model for a database according to the present disclosure includes: a selecting unit configured to select at least two CNN models from multiple CNN models whose classification capacity is known; a fitting unit configured to fit, based on the classification capacity and first parameters of the at least two CNN models, a curve taking classification capacity and a first parameter as variables; a predicting unit configured to predict, based on the curve, a first parameter of other CNN models; and a determining unit configured to determine a CNN model applicable to the database from the multiple CNN models. With the device and the method according to the present disclosure, there is no need to train all the CNN models, thereby greatly reducing the amount of computation and simplifying the process of designing the CNN model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A device for determining a convolutional neural network (CNN) model for a database, including: a selector configured to select at least two CNN models from multiple CNN models whose classification capacity is known; a fitter configured to fit, based on the classification capacity and first parameters of the at least two CNN models, a curve taking classification capacity and the first parameters as variables, the first parameter being used for characterizing performance of the CNN model; a predictor configured to predict, based on the curve, a first parameter of a CNN model of the multiple CNN models other than the at least two CNN models; and a determiner configured to determine a CNN model applicable to the database from the multiple CNN models based on the first parameters of each of the multiple CNN models. 2. The device according to claim 1, wherein the selector further includes: a first model determiner configured to determine, from the multiple CNN models, a one of the multiple CNN models whose classification capacity best matches the database as a first model; a second model determiner configured to determine, from the multiple CNN models, the one of the multiple CNN models whose classification capacity is a poorest as a second model; and a processor configured to determine the at least two CNN models, where the at least two CNN models include the first model and the second model. 3. The device according to claim 2, wherein the selector further includes a third model determiner configured to determine, from the multiple CNN models, the one of the models whose classification capacity is a best as a third model, and the at least two CNN models determined by the processor further include the third model. 4. The device according to claim 1, further including: a trainer configured to train each of the at least two CNN models, to obtain the first parameter of each CNN model. 5. The device according to claim 1, wherein the fitter is configured to fit the curve by means of one of a power exponent function and a logarithmic function. 6. The device according to claim 1, wherein the first parameter includes a precision parameter for characterizing classification precision of the CNN model. 7. The device according to claim 1, wherein the determiner is further configured to determine, from the multiple CNN models, the CNN model applicable to the database based on the first parameter and a second parameter of each of the multiple CNN models, the second parameter being used for characterizing performance of the CNN model. 8. The device according to claim 7, wherein the second parameter includes one of a speed parameter for characterizing a calculation speed of the CNN model and an occupancy rate parameter for characterizing a memory occupancy rate of the CNN model. 9. The device according to claim 8, wherein the speed parameter for characterizing the calculation speed of the CNN model includes a number of floating-point calculations of the CNN model. 10. A method for determining a convolutional neural network (CNN) model for a database, including: selecting at least two CNN models from multiple CNN models whose classification capacity is known; fitting, based on the classification capacity and first parameters of the at least two CNN models, a curve taking classification capacity and the first parameters as variables, the first parameter being used for characterizing performance of the CNN model; predicting, based on the curve, a first parameter of a CNN model of the multiple CNN models other than the at least two CNN models; and determining a CNN model applicable to the database from the multiple CNN models based on the first parameter of each of the multiple CNN models. 11. The method according to claim 10, wherein the selecting the at least two CNN models includes: determining, from the multiple CNN models, a one of the models whose classification capacity best matches the database as a first model; determining, from the multiple CNN models, the one of the models whose classification capacity is a poorest as a second model; and determining the at least two CNN models, where the at least two CNN models include the first model and the second model. 12. The method according to claim 11, wherein the selecting the at least two CNN models further includes: determining, from the multiple CNN models, the one of the models whose classification capacity is a best as a third model, and the at least two CNN models further include the third model. 13. The method according to claim 10, further including: training each of the at least two CNN models, to obtain the first parameter of each CNN model. 14. The method according to claim 10, wherein the curve is fitted by means of one of a power exponent function and a logarithmic function. 15. The method according to claim 10, wherein the first parameter includes a precision parameter for characterizing classification precision of the CNN model. 16. The method according to claim 10, wherein the determining the CNN model applicable to the database includes: determining, from the multiple CNN models, the CNN model applicable to the database based on the first parameter and a second parameter of each of the multiple CNN models, the second parameter being used for characterizing performance of the CNN model. 17. The method according to claim 16, wherein the second parameter includes one of a speed parameter for characterizing a calculation speed of the CNN model and an occupancy rate parameter for characterizing a memory occupancy rate of the CNN model. 18. The method according to claim 17, wherein the speed parameter for characterizing the calculation speed of the CNN model includes a number of floating-point calculations of the CNN model. 19. A non-transitory machine-readable storage medium in which a program product including machine-readable instruction codes stored therein is carried, wherein when being read and executed by a computer, the machine-readable instruction codes enables the computer to perform the method according to claim 10.