@@ -16,12 +16,8 @@ We augment the HRNet with a very simple segmentation head shown in the figure be
...
@@ -16,12 +16,8 @@ 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
HRNetV2 Segmentation models are now available. All the results are reproduced by using this repo!!!
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.
### Big models
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.