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Recently, I came across the problem of under-represented classes in an instance segmentation dataset. And it posed such problem, eg if I am labeling house objects, and stairs-bannisters , wall-painting are classes. Now, if I simply increase images, bannisters also increase exponentially,
But on the otherhand, if I can augment the dataset with the flexibility of Cropping only around the wall-painting class, I can reduce this data-imbalance by some extent..
Possible implementation
I don't have complete solution, but I tried a bit. I think, as we can pass class-name or class- id in the boxes once we pass it to transform , somehow if we can limit for a particular-transform(possibly a new one), class-conditional-cropping, and it only randomly select from selected classes, and it crops around those objects...
The text was updated successfully, but these errors were encountered:
Feature description
Recently, I came across the problem of under-represented classes in an instance segmentation dataset. And it posed such problem, eg if I am labeling house objects, and
stairs-bannisters
,wall-painting
are classes. Now, if I simply increase images, bannisters also increase exponentially,But on the otherhand, if I can augment the dataset with the flexibility of Cropping only around the wall-painting class, I can reduce this data-imbalance by some extent..
Possible implementation
I don't have complete solution, but I tried a bit. I think, as we can pass class-name or class- id in the
boxes
once we pass it to transform , somehow if we can limit for a particular-transform(possibly a new one),class-conditional-cropping
, and it only randomly select from selected classes, and it crops around those objects...The text was updated successfully, but these errors were encountered: