With modified architecture and initialization this ResNet50 version gives ~0.5% better accuracy than original.
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 smallperformance 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 Volta, Turing, and 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.
We are currently working on adding NHWC data layout support for Mixed Precision training.
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