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feat: Add minimum_box_area_ratio to filter out poor, partial boxes#2484
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thomas-coldwell wants to merge 2 commits intokeras-team:masterfrom
Open
feat: Add minimum_box_area_ratio to filter out poor, partial boxes#2484thomas-coldwell wants to merge 2 commits intokeras-team:masterfrom
minimum_box_area_ratio to filter out poor, partial boxes#2484thomas-coldwell wants to merge 2 commits intokeras-team:masterfrom
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kennyweigel
approved these changes
Aug 29, 2024
| crop_size=None, | ||
| bounding_box_format=None, | ||
| interpolation="bilinear", | ||
| minimum_box_area_ratio=0.0, |
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let us remove this from the augmentation layers. We need to keep this consistent with the other layers. Let us not touch this.
divyashreepathihalli
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Sep 3, 2024
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divyashreepathihalli
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Thanks for the PR! Left a few comments!
| target_size=(512, 512), | ||
| scale_factor=(3 / 4, 4 / 3), | ||
| bounding_box_format="xyxy", | ||
| minimum_box_area_ratio=0.5 |
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remove this as well. Maybe add a doc string to clip_to_image, explain the args and add an example there
| images=images, | ||
| image_shape=image_shape, | ||
| ) | ||
| passed = ops.logical_and(areas > 0.0, area_ratios > minimum_box_area_ratio) |
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can we rename passed to something more meaningful - valid_box maybe?
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What does this PR do?
This PR adds logic to the
bounding_box.clip_to_imageto attenuate the boxes that fall below a certain threshold of their original area in an image. This is particularly useful when creating a training dataset and wanting to ensure partial elements of an object that could now belong to any class e.g. a foot are not detected as a particular class leading to a noisy output during inference.This can be tested with the jittered resize demo by setting this value to 0.95 (most boxes will be filtered out as at least 5% has been cropped / clipped out) and then setting it to 0.0 which will make it behave as before.
@divyashreepathihalli and @sampathweb
Fixes # (issue)
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