We provide correct dilated pre-trained ResNet and DenseNet (stride of 8) for semantic segmentation.
For dilation of DenseNet, we provide
All provided models have been verified.
This code is provided together with the paper
- Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, Amit Agrawal. “Context Encoding for Semantic Segmentation” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018
Dilated ResNet and DenseNet
ResNet(block, layers, num_classes=1000, norm_layer=None)¶
Dilated Pre-trained ResNet Model, which preduces the stride of 8 featuremaps at conv5.
- He, Kaiming, et al. “Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- Yu, Fisher, and Vladlen Koltun. “Multi-scale context aggregation by dilated convolutions.”
DenseNet(growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000)¶
For correctly dilation of transition layer fo DenseNet, we implement the
- Huang, Gao, et al. “Densely Connected Convolutional Networks” CVPR 2017