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View Code? Open in Web Editor NEWThe iNaturalist Localization Dataset from "On Label Granularity and Object Localization" (ECCV 2022).
Home Page: https://arxiv.org/abs/2207.10225
License: MIT License
The iNaturalist Localization Dataset from "On Label Granularity and Object Localization" (ECCV 2022).
Home Page: https://arxiv.org/abs/2207.10225
License: MIT License
Hi!
Thanks for sharing iNatLoc500 dataset with the perfect hierarchical information! I found it very useful and helpful as an evaluation set for detection tasks.
I am actually using iNatLoc500 dataset for my research. I found the image sizes annotation provided in the dataset is quiet different from the actual image sizes. The difference are mainly caused by the swapped width and height fields in the annotation.
For instance, the test image named "test/Actinopterygii/Cyprinus carpio/05405c328abddbcbbf7596d33b7074d7.jpg" has annotation "test/Actinopterygii/Cyprinus carpio/05405c328abddbcbbf7596d33b7074d7.jpg,800,600". But the actual image size is w=800, h=600 as shown below:
In total, I found 12,085/12,500 erros in val split, and 12,066/12,500 in test split. I can make a pull request to upload the two .JSON files that recorded the difference between the annotation and the actual image sizes, and the corrected image size annotation files for val and test split.
I'd like to ask, is this caused by some rotation in the image data? Moreoever, do the bounding boxes annotations for val and test split also have corresponding errors due to the swapping? Thanks for your answer in advance! It is very important for my evaluation to know if there are also annotation drifts in the bboxes.
Thanks,
Best regards,
Mingxuan Liu
Thanks for sharing the dataset! Can you share the taxonomy of the hierarchies for this data? Ideally to load with networkx, but if it is not in that format, that should be fine too.
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