ResNet50 v1.5 MXNet checkpoint trained on Imagenet with AMP
Model Overview
With modified architecture and initialization this ResNet50 version gives ~0.5% better accuracy than original.
Model Architecture
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
Training
This model was trained using script available on NGC and in GitHub repo.
Dataset
The following datasets were used to train this model:
- ImageNet - Image database organized according to the WordNet hierarchy, in which each noun is depicted by hundreds and thousands of images.
Performance
Performance numbers for this model are available in NGC.
References
License
This model was trained using open-source software available in Deep Learning Examples repository. For terms of use, please refer to the license of the script and the datasets the model was derived from.