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competitions try1
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talrid committed Jun 5, 2020
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> achieve state-of-the-art accuracy on competitive datasets such as
> Stanford cars (96.0\%), CIFAR-10 (99.0\%), CIFAR-100 (91.5\%) and
> Oxford-Flowers (99.1\%)
## CVPR-Kaggle competitions
We participated and won top places in two
major CVPR-Kaggle competitions:
* [2nd place](https://www.kaggle.com/c/herbarium-2020-fgvc7/discussion/154186)
in Herbarium 2020 competition, out of 153 teams.
* [7th place](https://www.kaggle.com/c/plant-pathology-2020-fgvc7/discussion/154086)
in Plant-Pathology 2020 competition, out of 1317 teams.
<br> *TResNet* was a vital part of our solution for both competitions,
allowing us to work on high resolutions and reach top scores while
doing fast and efficient experiments.
<table class="tg">
<td class="tg-c3ow"><img src="./figures/herbarium_2020.png" align="center" width="200" ></td>
</table>
## Sotabench Comparisons
Comparative results from
[sotabench benchamrk](https://sotabench.com/benchmarks/image-classification-on-imagenet#code),
Binary file added figures/herbarium_2020.png
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