System and method for classification of ambiguous objects
The method for classifying ambiguous objects, including: determining initial labels for an image set; determining N training sets from the initially-labelled image set; training M annotation models using the N training sets; determining secondary labels for each image of the image set using the M trained annotation models; and determining final labels for the image set based on the secondary labels. The method can optionally include training a runtime model using images from the image set labeled with the final labels; and optionally using the runtime model.
1. A method for ambiguous object classification, comprising:
receiving an image set, wherein each image of the image set is labeled with a noisy label of an ambiguous object class;
partitioning the image set into N training sets;
training annotation models using the N training sets;
generating a set of secondary labels for each image of the image set using the trained annotation models; and
determining a final label for images of the image set based on the respective set of secondary labels.
2. The method of claim 1 , wherein the N training sets are split into K orders, wherein each order comprises all images of the image set.
3. The method of claim 2 , wherein training sets belonging to the same order are non-overlapping.
4. The method of claim 2 , wherein training sets belonging to the same order are the same size.
5. The method of claim 1 , further comprising training a runtime model using the image set associated with the final labels.
6. The method of claim 5 , further comprising:
selecting an operation program based on a determined classification from the runtime model; and
operating an appliance according to the operation program.
7. The method of claim 1 , wherein the image set is received from a set of appliances.
8. The method of claim 1 , wherein the image set comprises images that depict a view from above a scene.
9. The method of claim 1 , wherein the ambiguous object class comprises a food type.
10. The method of claim 1 , wherein each of the annotation models is trained using a different training set of the N training sets.
11. The method of claim 1 , wherein the set of secondary labels for a given image is generated using the trained annotation models that were not trained using the image.
12. The method of claim 1 , wherein when more than a threshold number of secondary labels disagree for a given image, determining the final label for the image comprises facilitating reannotation of the image.
13. The method of claim 1 , further comprising removing an image from the image set when more than a threshold number of secondary labels for the image disagree.
14. The method of claim 1 , wherein the final label for a given image is determined based on a majority vote between the secondary labels within the set of secondary labels for the image.
15. A non-transitory computer-readable storage medium storing instructions that, when executed by a processing system, cause the processing system to perform a method comprising:
receiving an image set, wherein each image of the image set is labeled with a noisy label of an ambiguous object class;
partitioning the image set into N training sets;
training annotation models using the N training sets;
generating a set of secondary labels for each image of the image set using the trained annotation models;
determining a final label for each image based on the respective set of secondary labels; and
training a runtime model using the image set and the final labels.
16. The non-transitory computer-readable storage medium of claim 15 , wherein the method further comprises:
receiving an inference image from an appliance;
selecting an operation program based on a determined classification for the inference image from the runtime model; and
operating the appliance according to the operation program.
17. The non-transitory computer-readable storage medium of claim 15 , wherein the N training sets are split into K orders, wherein each order comprises all images of the image set.
18. The non-transitory computer-readable storage medium of claim 15 , wherein the final label for a given image is determined based on agreement between a threshold number of secondary labels for the image.