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Unverified Commit 90578311 authored by Jingdong Wang's avatar Jingdong Wang Committed by GitHub
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Update README.md

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...@@ -17,7 +17,7 @@ We augment the HRNet with a very simple segmentation head shown in the figure be ...@@ -17,7 +17,7 @@ We augment the HRNet with a very simple segmentation head shown in the figure be
Besides, we further combine HRNet with [Object Contextual Representation](https://arxiv.org/pdf/1909.11065.pdf) and achieve higher performance on the three datasets. The code of HRNet+OCR is contained in this branch. Besides, we further combine HRNet with [Object Contextual Representation](https://arxiv.org/pdf/1909.11065.pdf) and achieve higher performance on the three datasets. The code of HRNet+OCR is contained in this branch.
## Segmentation models ## Segmentation models
The models are initialized by the weights pretrained on the ImageNet. You can download the pretrained models from https://github.com/HRNet/HRNet-Image-Classification. The models are initialized by the weights pretrained on the ImageNet. You can download the pretrained models from https://github.com/HRNet/HRNet-Image-Classification. *Slightly different, we use align_corners = True for upsampling in HRNet*.
1. Performance on the Cityscapes dataset. The models are trained and tested with the input size of 512x1024 and 1024x2048 respectively. 1. Performance on the Cityscapes dataset. The models are trained and tested with the input size of 512x1024 and 1024x2048 respectively.
If multi-scale testing is used, we adopt scales: 0.5,0.75,1.0,1.25,1.5,1.75. If multi-scale testing is used, we adopt scales: 0.5,0.75,1.0,1.25,1.5,1.75.
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