Method for configuring an image evaluation device for determining an object type (OT, OT1, OT2) and object sub-type of an imaged object, wherein a) a multiplicity of training images (TPIC) respectively assigned to an object type (OT, OT1, OT2) and an object sub-type are fed into a first neural network module for recognizing image features, b) training output data sets of the first neural network module are fed into a second neural network module for recognizing object types on the basis of image features, c) the first and second neural network modules (CNN, MLP) are jointly trained to the effect that training output data sets of the second neural network module at least approximately reproduce the object types assigned to the training images (TPIC), and d) for a respective object type (OT1, OT2): - training images (TPIC) assigned to this object type (OT1, OT2) are fed into the trained first neural network module with reuse of the trained first neural network module, - the latter's training output data set (FEA1, FEA2) generated for a respective training image (TPIC) is assigned to the object sub-type of the respective training image (TPIC), and - these sub-type assignments are used as a basis to configure a sub-type recognition module (BMLP1, BMLP2) for recognizing object sub-types on the basis of image features for the image evaluation device, wherein for each object type in each case a dedicated sub-type recognition module is specifically configured for recognizing object sub-types on the basis of image features, by means of the training images TPIC (OT, OST) being fed to a distribution module of the image evaluation device there is distribution among object-type-specific configuration pipelines by said module depending on a respectively assigned object type (OT1, OT2), wherein the training images TPIC (OT1, OST) assigned to the object type are fed to the trained first neural network module, which generates therefrom image features specific to the object type as training output data sets, wherein the image features of a respective training image TPIC(OT1, OST) are assigned to the object sub-type of this training image and with this assignment are communicated to a probabilistic classifier (BMLP1) as sub-type recognition module that is specific to the object type (OPTI) wherein these object assignments are used as a basis to configure the probabilistic classifier (BMLP1) for recognizing object sub-types of this object type (OPTI) a) a multiplicity of training images (TPIC) respectively assigned to an object type (OT, OT1, OT2) and an object sub-type are fed into a first neural network module for recognizing image features, b) training output data sets of the first neural network module are fed into a second neural network module for recognizing object types on the basis of image features, c) the first and second neural network modules (CNN, MLP) are jointly trained to the effect that training output data sets of the second neural network module at least approximately reproduce the object types assigned to the training images (TPIC), and d) for a respective object type (OT1, OT2): - training images (TPIC) assigned to this object type (OT1, OT2) are fed into the trained first neural network module with reuse of the trained first neural network module, - the latter's training output data set (FEA1, FEA2) generated for a respective training image (TPIC) is assigned to the object sub-type of the respective training image (TPIC), and - these sub-type assignments are used as a basis to configure a sub-type recognition module (BMLP1, BMLP2) for recognizing object sub-types on the basis of image features for the image evaluation device, wherein for each object type in each case a dedicated sub-type recognition module is specifically configured for recognizing object sub-types on the basis of image features, - training images (TPIC) assigned to this object type (OT1, OT2) are fed into the trained first neural network module with reuse of the trained first neural network module, - the latter's training output data set (FEA1, FEA2) generated for a respective training image (TPIC) is assigned to the object sub-type of the respective training image (TPIC), and - these sub-type assignments are used as a basis to configure a sub-type recognition module (BMLP1, BMLP2) for recognizing object sub-types on the basis of image features for the image evaluation device, wherein for each object type in each case a dedicated sub-type recognition module is specifically configured for recognizing object sub-types on the basis of image features, by means of the training images TPIC (OT, OST) being fed to a distribution module of the image evaluation device there is distribution among object-type-specific configuration pipelines by said module depending on a respectively assigned object type (OT1, OT2), wherein the training images TPIC (OT1, OST) assigned to the object type are fed to the trained first neural network module, which generates therefrom image features specific to the object type as training output data sets, wherein the image features of a respective training image TPIC(OT1, OST) are assigned to the object sub-type of this training image and with this assignment are communicated to a probabilistic classifier (BMLP1) as sub-type recognition module that is specific to the object type (OPTI) wherein these object assignments are used as a basis to configure the probabilistic classifier (BMLP1) for recognizing object sub-types of this object type (OPTI)