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Kate/splitter cli #81
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Please check the updated class descriptions for correctness.
Future updates could include:
- ignoring attributes in classification split (for captions, descriptions and other technical attributes)
- splitting using an attribute as label in classification split
- using polygons and masks in detection split
Produces a split with a specified ratio of images, avoiding having same | ||
labels in different subsets.|n |
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Here, we avoid having the same person id
or object id
. It could be label
or attribute
if attr_for_id
is specified.
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One more thing is, actually train
and val
set share person id
or object id
. (Most person re-identification data doesn't have val
set though). But they do not share IDs
with test
set.
I'm not sure how accurate the explanation should be.
If you feel the current explanation is sufficient, please leave it as it is.
Thank you for revising the descriptions.
|
Optional, enabled by default.
I mean using a single attribute, like in re-id. Maybe, using some subset of them / ignoring some attributes.
In Mask R-CNN they are intermixed with segmentation task. I, personally, consider these types of annotations more or less interchangeable, because all these types can be used for training a segmentation and a detection algorithm. |
…SpecificSplit), 3. revise test code
Summary
This PR includes
How to test
Unittest
Testing classification split with imagenet dataset.
Testing detection split with voc dataset
Testing re-identification split with imagenet dataset.
Checklist
develop
branchLicense
Feel free to contact the maintainers if that's a concern.