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

1. An apparatus comprising:

interface circuitry to receive first and second images corresponding to a product;

machine readable instructions; and

at least one processor circuit to execute the machine readable instructions to at least:

detect a first label in the first image, the first label defined by a first bounding box;

classify the first label based on the first bounding box;

detect a second label in the second image, the second label defined by a second bounding box;

classify the second label based on the second bounding box; and

when the first classification and the second classification are associated with the product, assign the first and second classifications to the product.

2. The apparatus of claim 1 , wherein the first image corresponds to a first portion of the product and the second image corresponds to a second portion of the product, the first portion to be at least partially offset relative to the second portion.

3. The apparatus of claim 1 , wherein one or more of the at least one processor circuit is to detect the first label by executing the machine readable instructions to:

extract a feature map from the first image, the feature map including points; and

apply a region proposal network to the feature map to:

generate, at respective ones of the points in the feature map, a set of anchor boxes based on predetermined anchor box sizes and anchor box ratios;

identify ones of the anchor boxes that include the first label, the ones of the anchor boxes to include a respective confidence score;

determine bounding box coordinates for the ones of the anchor boxes, the bounding box coordinates corresponding to positions relative to the first image; and

identify the first bounding box as including the label by applying a non-maximum selection technique to the ones of the anchor boxes.

4. The apparatus of claim 3 , wherein the one or more of the at least one processor circuit is to execute the machine readable instructions to train the region proposal network by configuring a first hyperparameter corresponding to the predetermined anchor box ratios and a second hyperparameter corresponding to the predetermined anchor box sizes.

5. The apparatus of claim 4 , wherein the one or more of the at least one processor circuit is to execute the machine readable instructions to configure the first hyperparameter to anchor box ratios of (a) 1:2, (b) 1:1, and (c) 2:1.

6. The apparatus of claim 4 , wherein the one or more of the at least one processor circuit is to execute the machine readable instructions to configure the second hyperparameter to anchor box scales of 2, 4, and 6.

7. The apparatus of claim 1 , wherein one or more of the at least one processor circuit is to execute the machine readable instructions to:

detect a third label in the second image, the third label defined by a third bounding box;

classify the third label based on the third bounding box; and

assign the third classification to the product.

8. A non-transitory machine readable storage medium comprising instructions to cause at least one processor circuit to at least:

detect a first label in a first image, the first label defined by a first bounding box, the first image to correspond to a first product;

classify the first label based on the first bounding box;

detect a second label in a second image, the second label defined by a second bounding box, the second image to correspond to the first product;

classify the second label based on the second bounding box; and

when the first classification and the second classification are associated with the product, assign the first and second classifications to the product.

9. The non-transitory machine readable storage medium of claim 8 , wherein the first image corresponds to a first region of the product and the second image corresponds to a second region of the product, the first region to be at least partially different than the second region.

10. The non-transitory machine readable storage medium of claim 8 , wherein the instructions cause one or more of the at least one processor circuit to:

extract a feature map from the first image, the feature map including points;

generate, by applying a region proposal network to the feature map, a set of anchors boxes at respective ones of the points in the feature map based on predetermined anchor box sizes and anchor box ratios;

identify ones of the anchor boxes that include the first label, the ones of the anchor boxes to include a respective confidence score;

determine bounding box coordinates for the ones of the anchor boxes, the bounding box coordinates corresponding to positions relative to the first image; and

identify the first bounding box as including the first label by applying a non-maximum selection technique to the ones of the anchor boxes.

11. The non-transitory machine readable storage medium of claim 10 , wherein the instructions cause the one or more of the at least one processor circuit to train the region proposal network by configuring a first hyperparameter corresponding to the predetermined anchor box ratios and a second hyperparameter corresponding to the predetermined anchor box sizes.

12. The non-transitory machine readable storage medium of claim 11 , wherein the instructions are to cause the one or more of the at least one processor circuit to configure the first hyperparameter to anchor box ratios of (a) 1:2, (b) 1:1, and (c) 2:1.

13. The non-transitory machine readable storage medium of claim 11 , wherein the instructions are to cause the one or more of the at least one processor circuit to configure the second hyperparameter to anchor box scales of 2, 4, and 6.

14. The non-transitory machine readable storage medium of claim 8 , wherein the instructions are to cause the one or more of the at least one processor circuit to:

detect a third label in the second image, the third label defined by a third bounding box;

classify the third label based on the third bounding box; and

assign the third classification to the product.

15. A method comprising:

detecting, by executing a machine readable instruction with programmable circuitry, a first label in a first image, the first label defined by a first bounding box, the first image corresponding to a product;

classifying, by executing a machine readable instruction with the programmable circuitry, the first label based on the first bounding box;

detecting, by executing a machine readable instruction with the programmable circuitry, a second label in a second image, the second label defined by a second bounding box the second image corresponding to the product;

classifying, by executing a machine readable instruction with the programmable circuitry, the second label based on the second bounding box; and

when the first classification and the second classification are associated with the product, assigning, by executing a machine readable instruction with the programmable circuitry, the first and second classifications to the product.

16. The method of claim 15 , wherein the first image corresponds to a first portion of the product and the second image corresponds to a second portion of the product, the second portion to be at least partially offset from the first portion of the product.

17. The method of claim 15 , wherein the detecting of the first label includes:

extracting a feature map from the first image, the feature map including points; and

applying a region proposal network to the feature map, wherein the applying of the region proposal network to includes:

generating, at respective ones of the points in the feature map, a set of anchors boxes based on predetermined anchor box sizes and anchor box ratios;

identifying ones of the anchor boxes that include the first label, the ones of the anchor boxes to include a respective confidence score;

determining bounding box coordinates for the ones of the anchor boxes, the bounding box coordinates corresponding to positions relative to the first image; and

identifying the first bounding box as including the label by applying a non-maximum selection technique to the ones of the anchor boxes.

18. The method of claim 17 , further including training the region proposal network by configuring a first hyperparameter corresponding to the predetermined anchor box ratios and a second hyperparameter corresponding to the predetermined anchor box sizes.

19. The method of claim 18 , wherein the configuring of the first hyperparameter includes tuning the first hyperparameter to generate anchor box ratios of (a) 1:2, (b) 1:1, and (c) 2:1.

20. The method of claim 18 , wherein the configuring of the second hyperparameter includes tuning the second hyperparameter to generate anchor box scales of 2, 4, and 6.