With modified architecture and initialization this ResNet50 version gives ~0.5% better accuracy than original.
The model architecture was present in Deep Residual Learning for Image Recognition paper. The main advantage of the model is the usage of residual layers as a building block that helps with gradient propagation during training.
Image source: Deep Residual Learning for Image Recognition
This model was trained using script available on NGC and in GitHub repo
The following datasets were used to train this model:
Performance numbers for this model are available in NGC