DeepLearningExamples/TensorFlow/Classification/ConvNets

Resnet-family Convolutional Neural Networks for Image Classification in Tensorflow

In this repository you will find implementation of Resnet and its variations for image classification

Table Of Contents

• Models
• Validation accuracy results
• Training performance results
  • Training performance: NVIDIA DGX-1 (8x V100 16G)
• Release notes
  • Changelog

Models

The following table provides links to where you can find additional information on each model:

Model	Link
resnet50	README
resnext101-32x4d	README
se-resnext101-32x4d	README

Validation accuracy results

Our results were obtained by running the applicable training scripts in the tensorflow-20.03-tf1-py3 NGC container on NVIDIA DGX-1 with (8x V100 16G) GPUs. The specific training script that was run is documented in the corresponding model's README.

The following table shows the validation accuracy results of the three classification models side-by-side.

arch	AMP Top1	AMP Top5	FP32 Top1	FP32 Top5
resnet50	78.35	94.21	78.34	94.21
resnext101-32x4d	80.21	95.00	80.21	94.99
se-resnext101-32x4d	80.87	95.35	80.84	95.37

Training performance results

Training performance: NVIDIA DGX-1 (8x V100 16G)

Our results were obtained by running the applicable training scripts in the tensorflow-20.03-tf1-py3 NGC container on NVIDIA DGX-1 with (8x V100 16G) GPUs. Performance numbers (in images per second) were averaged over an entire training epoch. The specific training script that was run is documented in the corresponding model's README.

The following table shows the training accuracy results of the three classification models side-by-side.

arch	Mixed Precision	Mixed Prcesision XLA	FP32	Mixed Precision speedup	XLA Mixed Precision speedup
resnet50	8277.91 img/s	9485.21 img/s	2785.81 img/s	2.97x	1.14x
resnext101-32x4d	3151.81 img/s	4231.42 img/s	1055.82 img/s	2.98x	1.34x
se-resnext101-32x4d	2168.40 img/s	3297.39 img/s	921.38 img/s	2.35x	1.52x

Release notes

Changelog

April 2020

• Initial release