Provided are device and method for determining a Convolutional Neural Network (CNN) model. The device for determining the CNN model includes: a first determination unit configured to determine complexity of a database including multiple samples; a second determination unit configured to determine a classification capability of a CNN model applicable to the database based on the complexity of the database; a third determination unit configured to acquire classification capability of each candidate CNN model; and a matching unit configured to determine the CNN model applicable to the database based on the classification capability of each candidate CNN model. With the device and method for determining the CNN module, a design process of CNN model can be simplified.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A device for determining a Convolutional Neural Network (CNN) model, including: a first determiner configured to determine complexity of a database including multiple samples; a second determiner configured to determine a classification capability of the CNN model applicable to the database based on the complexity of the database; a third determiner configured to acquire a classification capability of each of multiple candidate CNN models; and a matcher configured to determine the CNN model applicable to the database based on the classification capability of each candidate CNN model and the classification capability of the CNN model applicable to the database. 2. The device according to claim 1, wherein the first determiner selects multiple parameters from a parameter group and determines the complexity of the database based on selected multiple parameters, and the parameter group includes: a number of categories representing sample categories for multiple samples in the database; an average number of samples representing an average value of numbers of samples of the categories in the database; a sample equalization degree representing an equalization degree among the numbers of the samples of the categories in the database; a background complexity representing a background complexity of an image background of one of the samples in the database; and a target clarity representing a difficulty of identifying a target from the one of the samples in the database. 3. The device according to claim 2, wherein the first determiner may be configured to determine a weight and score of each of the multiple parameters and determine the complexity of the database by weighting the score of each of the multiple parameters and summing weighted scores. 4. The device according to claim 3, wherein the first determiner forms a comparison matrix based on relative importance between each of two parameters of the multiple parameters by using an analytic hierarchy process, and determines the weight of each of the multiple parameters based on the comparison matrix. 5. The device according to claim 1, wherein the second determiner is configured to determine a value range of the classification capability of the CNN model applicable to the database based on the complexity of the database. 6. The device according to claim 1, wherein the third determiner is configured to determine a first parameter of the candidate CNN model representing a full extent to which the candidate CNN model is trained, and calculate the classification capability of the candidate CNN model based on the first parameter of the candidate CNN model. 7. The device according to claim 6, wherein the third determiner is further configured to calculate the classification capability of the candidate CNN model based on the first parameter, a second parameter and a third parameter of the candidate CNN model, where the second parameter represents a number of convolution layers of the candidate CNN model, and the third parameter represents a width parameter of the candidate CNN model representing a multiplication factor of the number of feature maps of the candidate CNN model. 8. The device according to claim 1, wherein the third determiner is further configured to establish a correspondence between candidate CNN models and classification capabilities, and wherein the matcher is configured to determine the candidate CNN model corresponding to the classification capability of the CNN model applicable to the database as the CNN model applicable to the database. 9. The device according to claim 8, wherein the third determiner is configured to group multiple candidate CNN models to make the candidate CNN models having one of a same and similar classification capabilities into a same group, and make the candidate CNN models within the same group correspond to a representative classification capability of one of the same and the similar classification capabilities corresponding to the group. 10. The device according to claim 7, further including a selecter configured to select multiple candidate CNN models. 11. The device according to claim 10, wherein the selecter is configured to establish multiple sample CNN models, and select a sample CNN model, of which a second parameter and a third parameter satisfy a predetermined condition, as the candidate CNN model. 12. The device according to claim 11, wherein the selecter is configured to establish each of the multiple sample CNN models as a number of feature maps after each of one or more pooling layers of the sample CNN model is two times the number of feature maps before the pooling layer. 13. The device according to claim 10, wherein the selecter is configured to establish each of the multiple sample CNN models by setting a convolution template for each convolution layer of the sample CNN model and a pooling template for each pooling layer of the sample CNN model. 14. The device according to claim 10, wherein the selecter is configured to establish each of the multiple sample CNN models by setting a Rectified Linear Unit (ReLU) function as an activation function of the sample CNN model. 15. A method for determining a Convolutional Neural Network (CNN) model, including: determining complexity of a database including multiple samples; determining a classification capability of a CNN model applicable to the database based on the complexity of the database; acquiring classification capability of each of multiple candidate CNN models; and determining the CNN model applicable to the database based on the classification capability of each candidate CNN model and the classification capability of the CNN model applicable to the database. 16. The method according to claim 15, wherein the determining the complexity of the database including the multiple samples includes: selecting multiple parameters from a parameter group and determining the complexity of the database based on selected multiple parameters, and the parameter group includes: a number of categories representing a number of sample categories for multiple samples in the database; an average number of samples representing an average value of the numbers of samples of respective categories in the database; a sample equalization degree representing an equalization degree among the numbers of samples of the respective categories in the database a background complexity, representing a background complexity of an image background of one of the samples in the database; and a target clarity representing a difficulty of identifying a target from the samples in the database. 17. The method according to claim 16, wherein the determining the complexity of the database based on the multiple parameters includes: determining a weight and score of each of the multiple parameters; and determining the complexity of the database by weighting the score of each of the multiple parameters and summing weighted scores. 18. The method according to claim 17, wherein the determining the weight of respective parameters includes: forming a comparison matrix based on relative importance between each of two parameters of the multiple parameters by using an analytic hierarchy process; and determining the weight of each of the multiple parameters based on the comparison matrix. 19. The method according to claim 15, wherein the determining the classification capability of the CNN model applicable to the database based on the complexity of the database includes: determining a value range of the classification capability of the CNN model applicable to the database based on the complexity of the database. 20. A non-transitory machine readable storage medium, on which a program product including machine readable instruction codes stored therein is carried, where, when reading and executing the instruction codes, a computer can perform the method according to claim 15.