Systems and methods for presenting image classification results

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 interactive 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 a convolutional neural network and associated model hyperparameters, and wherein the associated model hyperparameters comprise at least one of a number of layers, a number of nodes, and an indication of whether the network is fully connected;

an input icon;

and a first button;

upon receiving a user selection of at least one of the first interactive icons:

performing a search to identify second results, the search being based on the selected at least one of the first interactive icons; and

generating a second graphical user interface displaying the second results, the second graphical user interface being different from the first graphical user interface; and

upon receiving a user selection of the first button:

determining whether the input icon is empty;

in response to determining the input icon is not empty, transmitting, to a server, the image and content in the input icon;

receiving, from the server, 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 including at least one of make, model, trim and color;

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 including at least one of make, model, trim and color is modified, wherein retraining the classification model to include the updated model hyperparameters comprises developing the convolutional neural network using backpropagation with gradient descent based on a training dataset; and

performing a conditional routine to substitute third results based on the classification model exception.

2. The system of claim 1 , wherein the classification model comprises a convolutional neural network.

3. The system of claim 2 , wherein the patch comprises updates for connection layers of the convolutional neural network.

4. The system of claim 1 , wherein the second graphical user interface comprises:

vehicle images associated with the second results;

vehicle conditions associated with the second results; and

distances associated with the second results.

5. The system of claim 4 , wherein the second graphical interface further comprises financing option icons for the second results.

6. The system of claim 1 , wherein the patch comprises model exceptions based on the content in the input icon.

7. The system of claim 1 , wherein the operations further comprise updating the classification model by running the patch.

8. The system of claim 1 , wherein the patch is configured to automatically execute commands and invoke patch management systems in an operating system of the one or more processors.

9. The system of claim 1 , wherein the interactive icons display thumbnails of vehicles identified as preliminary results.

10. The system of claim 9 , wherein the interactive icons are configured to change color and transparency when selected.

11. The system of claim 1 , wherein:

the first graphical user interface further comprises a second button; and

the operations further comprise:

upon receiving a user selection of the second button, transmitting an error message to the server.

12. The system of claim 1 , wherein generating the second graphical user interface comprises displaying the second results in a ranking based on financing availability.

13. The system of claim 1 , wherein:

the first graphical user interface further comprises a second button; and

the operations further comprise:

upon receiving a user selection of the second button:

transmitting a repopulate request to the server;

removing the one or more first interactive icons from the first graphical user interface; and

displaying second interactive icons in the first graphical user interface.

14. The system of claim 13 , wherein:

the first graphical user interface further comprises a third button; and

the operations further comprise:

upon receiving a user selection of the third button:

transmitting, to the server, a query for available vehicles without filtering conditions.

15. The system of claim 1 , wherein:

the first graphical user interface further comprises a second button; and

the operations further comprise:

upon receiving a user selection of the second button, generating a third graphical user interface displaying an augmenter reality application.

16. The system of claim 1 , 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.

17. The system of claim 1 , wherein generating the first graphical user interface comprises preselecting at least one of the one or more first interactive icons based on confidence levels of the first results.

18. The system of claim 17 , wherein preselected first interactive icons are displayed in a different color in the first graphical user interface.

19. A computer-implemented method for generating and implementing patches to improve classification model results based on user feedback through input icons, the computer-implemented method comprising:

capturing an image with a camera;

generating a first graphical user interface comprising:

one or more interactive 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 a convolutional neural network and associated model hyperparameters, and wherein the associated model hyperparameters comprise at least one of a number of layers, a number of nodes, and an indication of whether the network is fully connected;

an input icon; and

a first button;

upon receiving a user selection of at least one of the interactive icons:

performing a search to identify second results, the search being based on the selected at least one of the interactive icons; and

generating a second graphical user interface displaying the second results, the second graphical user interface being different from the first graphical user interface; and

upon receiving a user selection of the first button:

determining whether the input icon is empty;

in response to determining the input icon is not empty, transmitting, to a server, the image and content in the input icon;

receiving, from the server, 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 including at least one of make, model, trim and color;

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 including at least one of make, model, trim and color is modified, wherein retraining the classification model to include the updated model hyperparameters comprises developing the convolutional neural network using backpropagation with gradient descent based on a training dataset; and

performing a conditional routine to substitute third results based on the classification model exception.

20. An apparatus for generating and implementing patches to improve classification model results based on user feedback through input icons, the apparatus comprising;

a camera;

at least one memory storing instructions; and

at least one processor programmed by the instructions to:

capture an image with the camera;

generate a first graphical user interface comprising:

one or more interactive 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 a convolutional neural network and associated model hyperparameters, and wherein the associated model hyperparameters comprise at least one of a number of layers, a number of nodes, and an indication of whether the network is fully connected;

an input icon; and

a first button;

upon receiving a user selection of at least one of the interactive icons:

perform a search to identify second results, the search being based on the selected at least one of the interactive icons; and

generate a second graphical user interface displaying the second results, the second graphical user interface being different from the first graphical user interface; and

upon receiving a user selection of the first button:

determine whether the input icon is empty;

in response to determining the input icon is not empty, transmit the image and content in the input icon to a server;

receive, from the server, 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 including at least one of make, model, trim and color;

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 including at least one of make, model, trim and color is modified, wherein retraining the classification model to include the updated model hyperparameters comprises developing the convolutional neural network using backpropagation with gradient descent based on a training dataset; and

performing a conditional routine to substitute third results based on the classification model exception.