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 (coming soon), please cite our work.
ResNet(block, layers, num_classes=1000)¶
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)¶
Dilated Densenet-BC model class
- Huang, Gao, et al. “Densely Connected Convolutional Networks” CVPR 2017