Methods, systems, articles of manufacture, and apparatus to classify labels based on images using artificial intelligence

1. An apparatus comprising:

memory; and

processing circuitry to execute computer readable instructions to implement:

a regional proposal network to:

determine a first bounding box for a first region of interest in a first input image of a product; and

determine a second bounding box for a second region of interest in a second input image of the product;

a neural network to:

generate a first classification for a first label in the first input image using the first bounding box; and

generate a second classification for a second label in the second input image using the second bounding box;

a comparator to determine that the first input image and the second input image correspond to a same product; and

a report generator to link the first classification and the second classification to the product.

2. The apparatus of claim 1 , wherein the first input image and the second input image are crowdsourced images taken from computing devices of users.

3. The apparatus of claim 1 , wherein the comparator is to determine that the first input image and the second input image correspond to the same product based on at least one of (a) a first file name of the first input image and a second file name of the second input image or (b) first metadata of the first file name and second metadata of the second file name.

4. The apparatus of claim 1 , further including an image resizer to resize at least one of the first input image or the second input image to a preset size.

5. The apparatus of claim 1 , wherein the report generator is to generate a report corresponding to the product including the first label and the second label.

6. The apparatus of claim 1 , wherein the report generator is to generate a report corresponding to product and the linking of the first and second classifications.

7. The apparatus of claim 1 , wherein the second input image of the product does not include the second label.

8. A non-transitory computer readable storage medium comprising instructions which, when executed, cause one or more processors to at least:

determine a first bounding box for a first region of interest in a first input image of a product;

determine a second bounding box for a second region of interest in a second input image of the product;

generate a first classification for a first label in the first input image using the first bounding box;

generate a second classification for a second label in the second input image using the second bounding box;

determine that the first input image and the second input image correspond to a same product; and

link the first classification and the second classification to the product.

9. The non-transitory computer readable storage medium of claim 8 , wherein the first input image and the second input image are crowdsourced images taken from computing devices of users.

10. The non-transitory computer readable storage medium of claim 8 , wherein the instructions cause the one or more processors to determine that the first input image and the second input image correspond to the same product based on at least one of (a) a first file name of the first input image and a second file name of the second input image or (b) first metadata of the first file name and second metadata of the second file name.

11. The non-transitory computer readable storage medium of claim 8 , wherein the instructions cause the one or more processors to resize at least one of the first input image or the second input image to a preset size.

12. The non-transitory computer readable storage medium of claim 8 , wherein the instructions cause the one or more processors to generate a report corresponding to the product including the first label and the second label.

13. The non-transitory computer readable storage medium of claim 8 , wherein the instructions cause the one or more processors to generate a report corresponding to product and the linking of the first and second classifications.

14. The non-transitory computer readable storage medium of claim 8 , wherein the second input image of the product does not include the second label.

15. A method comprising:

determining, by executing an instruction with a processor, a first bounding box for a first region of interest in a first input image of a product;

determining, by executing an instruction with the processor, a second bounding box for a second region of interest in a second input image of the product;

generating, using a neural network, a first classification for a first label in the first input image using the first bounding box;

generating, using the neural network, a second classification for a second label in the second input image using the second bounding box;

determining, by executing an instruction with the processor, that the first input image and the second input image correspond to a same product; and

linking, by executing an instruction with the processor, the first classification and the second classification to the product.

16. The method of claim 15 , wherein the first input image and the second input image are crowdsourced images taken from computing devices of users.

17. The method of claim 15 , further including determining that the first input image and the second input image correspond to the same product based on at least one of (a) a first file name of the first input image and a second file name of the second input image or (b) first metadata of the first file name and second metadata of the second file name.

18. The method of claim 15 , further including resizing at least one of the first input image or the second input image to a preset size.

19. The method of claim 15 , further including generating a report corresponding to the product including the first label and the second label.

20. The method of claim 15 , further including generating a report corresponding to product and the linking of the first and second classifications.