Systems and methods for presenting image classification results

Abstract

An apparatus for performing image searches including a camera, storage devices storing a set of instructions, and a processor coupled to the at least one storage device and the camera. The instructions configure the at least one processor to perform operations including identifying attributes of the captured image using a classification model; identifying first results based on the identified attributes; selecting a subset of first results based on corresponding probability scores, generating a first graphical user interface including interactive icons corresponding to first results in the subset, an input icon, and a first button. The operations may also include receiving a selection of the first button, performing a search to identify second results, and generating a second graphical user interface displaying the second results.

What is claimed is: 
     
         1 . A system for generating and implementing patches to improve classification model results based on user feedback through input icons, the system comprising:
 a camera;   one or more processors; and   one or more memory devices storing instructions that, when executed by the one or more processors, configure the one or more processors to perform operations comprising:
 capturing an image with the camera; 
 generating a first graphical user interface comprising one or more first icons corresponding to first results, the first results comprising object recognition results based on attributes identified in the image using a classification model, wherein the classification model comprises model hyperparameters; and 
 upon receiving a user selection of at least one of the first icons:
 performing a search to identify second results, the search being based on the selected at least one of the first icons; 
 generating a second graphical user interface displaying the second results, the second graphical user interface being different from the first graphical user interface; 
 receiving, from a server and based on the second results, a patch for the classification model, the patch comprising updated model hyperparameters and a classification model exception for the identified attributes, and wherein the patch includes a script that modifies a response of the classification model to images with attributes; 
 based on the patch, retraining the classification model to include the updated model hyperparameters such that the response of the classification model to images with attributes is modified, wherein retraining the classification model to include the updated model hyperparameters comprises developing the classification model based on a training dataset; and 
 performing a conditional routine to substitute third results based on the classification model exception. 
 
   
     
     
         2 . A method for generating and implementing patches to improve classification model results based on user feedback through input icons, the method comprising:
 capturing an image with a camera;   generating a first graphical user interface comprising
 one or more icons corresponding to first results, the first results comprising object recognition results based on attributes identified in the image using a classification model, wherein the classification model comprises model hyperparameters; and 
   upon receiving a user selection of at least one of the icons:
 performing a search to identify second results, the search being based on the selected at least one of the icons; 
 generating a second graphical user interface displaying the second results, the second graphical user interface being different from the first graphical user interface; 
 receiving, from a server and based on the second results, a patch for the classification model, the patch comprising updated model hyperparameters and a classification model exception for the identified attributes, and wherein the patch includes a script that modifies a response of the classification model to images with attributes; 
 based on the patch, retraining the classification model to include the updated model hyperparameters such that the response of the classification model to images with attributes is modified, wherein retraining the classification model to include the updated model hyperparameters comprises developing the classification model based on a training dataset; and 
 performing a conditional routine to substitute third results based on the classification model exception. 
   
     
     
         3 . The method of  claim 2 , wherein the classification model comprises a convolutional neural network. 
     
     
         4 . The method of  claim 3 , wherein the patch comprises updates for connection layers of the convolutional neural network. 
     
     
         5 . The method of  claim 2 , wherein the second graphical user interface comprises:
 images associated with the second results;   conditions associated with the second results; and   distances associated with the second results.   
     
     
         6 . The method of  claim 2 , further comprising, upon receiving a user selection of a first button of the first graphical user interface:
 determining whether an input icon of the first graphical user interface is empty; and   in response to determining the input icon is not empty, transmitting, to a server, the image and content in the input icon.   
     
     
         7 . The method of  claim 2 , wherein the classification model exception is based on content in the at least one of the icons. 
     
     
         8 . The method of  claim 2 , further comprising updating the classification model by running the patch. 
     
     
         9 . The method of  claim 2 , wherein the patch is configured to automatically execute commands and invoke patch management systems in an operating system. 
     
     
         10 . The method of  claim 2 , wherein the icons display thumbnails of vehicles identified as preliminary results. 
     
     
         11 . The method of  claim 10 , wherein the icons are configured to change color and transparency when selected. 
     
     
         12 . The method of  claim 2 , further comprising
 upon receiving a user selection of a first button of the first graphical user interface, transmitting an error message to the server.   
     
     
         13 . The method of  claim 2 , wherein generating the second graphical user interface comprises displaying the second results in a ranking based on financing availability. 
     
     
         14 . The method of  claim 2 , further comprising:
 upon receiving a user selection of a first button of the first graphical user interface:
 transmitting a repopulate request to the server; 
 removing the one or more icons from the first graphical user interface; and 
 displaying second icons in the first graphical user interface. 
   
     
     
         15 . The method of  claim 14 , further comprising:
 upon receiving a user selection of a second button of the first graphical user interface:
 transmitting, to the server, a query for available vehicles without filtering conditions. 
   
     
     
         16 . The method of  claim 2 , further comprising:
 upon receiving a user selection of a first button of the first graphical user interface, generating a third graphical user interface displaying an augmenter reality application.   
     
     
         17 . The method of  claim 2 , wherein generating the first graphical user interface comprises:
 retrieving visualization preferences from a local memory; and   determining the first results by truncating preliminary search results based on the visualization preferences.   
     
     
         18 . The method of  claim 2 , wherein generating the first graphical user interface comprises preselecting at least one of the one or more icons based on confidence levels of the first results. 
     
     
         19 . The method of  claim 18 , wherein preselected first icons are displayed in a different color in the first graphical user interface. 
     
     
         20 . One or more non-transitory, computer-readable media storing instructions that, when executed by one or more processors, cause operations comprising:
 capturing an image with a camera;   generating a first graphical user interface comprising
 one or more icons corresponding to first results, the first results comprising object recognition results based on attributes identified in the image using a classification model, wherein the classification model comprises model hyperparameters; and 
   upon receiving a user selection of at least one of the icons:
 performing a search to identify second results, the search being based on the selected at least one of the icons; 
 generating a second graphical user interface displaying the second results, the second graphical user interface being different from the first graphical user interface; 
 receiving, from a server and based on the second results, a patch for the classification model, the patch comprising updated model hyperparameters and a classification model exception for the identified attributes, and wherein the patch includes a script that modifies a response of the classification model to images with attributes; 
 based on the patch, retraining the classification model to include the updated model hyperparameters such that the response of the classification model to images with attributes is modified, wherein retraining the classification model to include the updated model hyperparameters comprises developing the classification model based on a training dataset; and 
 performing a conditional routine to substitute third results based on the classification model exception.