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. Convolutional Network models for TensorFlow1 are no longer maintained and will soon become unavailable, please consider PyTorch or TensorFlow2 models as a substitute for your requirements.

Table Of Contents

• Models
• Validation accuracy results
• Training performance results
  • Training performance: NVIDIA DGX A100 (8x A100 40G)
  • 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-20.06-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 A100 (8x A100 40G)

Our results were obtained by running the applicable training scripts in the tensorflow-20.06-tf1-py3 NGC container on NVIDIA DGX A100 with (8x A100 40G) 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 performance results of the three classification models side-by-side.

arch	Mixed Precision XLA	TF32 XLA	Mixed Precision speedup
resnet50	16400 img/s	6300 img/s	2.60x
resnext101-32x4d	8000 img/s	2630 img/s	3.05x
se-resnext101-32x4d	6930 img/s	2400 img/s	2.88x

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

Our results were obtained by running the applicable training scripts in the tensorflow-20.06-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 performance results of the three classification models side-by-side.

arch	Mixed Precision XLA	FP32 XLA	Mixed Precision speedup
resnet50	9510 img/s	3170 img/s	3.00x
resnext101-32x4d	4160 img/s	1210 img/s	3.44x
se-resnext101-32x4d	3360 img/s	1120 img/s	3.00x

Release notes

Changelog

April 2021

• Ceased maintenance of ConvNets in TensorFlow1

June 2020

• ConvNets repo restructurization
• Initial release of ResNext and SE-Resnext