ResNet50 checkpoint trained with AMP on ImageNet
Model Overview
With modified architecture and initialization this ResNet50 version gives ~0.5% better accuracy than original.
Model Architecture
The ResNet50 v1.5 model is a modified version of the original ResNet50 v1 model.
The difference between v1 and v1.5 is that in the bottleneck blocks which requires downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.
This difference makes ResNet50 v1.5 slightly more accurate (~0.5% top1) than v1 but comes with a small performance drawback (~5% imgs/sec).
The model is initialized as described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification
This model is trained with mixed precision using Tensor Cores on the NVIDIA Ampere GPU architectures. Therefore, researchers can get results over 2x faster than training without Tensor Cores while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.
Training
This model was trained using script available 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 GitHub readme performance section.
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.