2020-06-12 12:38:25 +02:00
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# Resnet-family Convolutional Neural Networks for Image Classification in Tensorflow
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In this repository you will find implementation of Resnet and its variations for image
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classification
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## Table Of Contents
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* [Models](#models)
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* [Validation accuracy results](#validation-accuracy-results)
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* [Training performance results](#training-performance-results)
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2020-06-27 09:24:41 +02:00
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* [Training performance: NVIDIA DGX A100 (8x A100 40G)](#training-performance-nvidia-dgx-a100-8x-a100-40g)
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* [Training performance: NVIDIA DGX-1 (8x V100 16G)](#training-performance-nvidia-dgx-1-8x-v100-16g)
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2020-06-12 12:38:25 +02:00
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* [Release notes](#release-notes)
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* [Changelog](#changelog)
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## Models
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The following table provides links to where you can find additional information on each model:
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| **Model** | **Link**|
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|-----------|---------|
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| resnet50 | [README](./resnet50v1.5/README.md) |
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| resnext101-32x4d | [README](./resnext101-32x4d/README.md) |
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| se-resnext101-32x4d | [README](./se-resnext101-32x4d/README.md) |
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## Validation accuracy results
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2020-06-27 09:24:41 +02:00
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Our results were obtained by running the applicable training scripts in the tensorflow-20.06-tf1-py3 NGC container
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2020-06-12 12:38:25 +02:00
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on NVIDIA DGX-1 with (8x V100 16G) GPUs. The specific training script that was run is documented in the corresponding model's README.
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The following table shows the validation accuracy results of the
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three classification models side-by-side.
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| **arch** | **AMP Top1** | **AMP Top5** | **FP32 Top1** | **FP32 Top5** |
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| resnet50 | 78.35 | 94.21 | 78.34 | 94.21 |
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| resnext101-32x4d | 80.21 | 95.00 | 80.21 | 94.99 |
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| se-resnext101-32x4d | 80.87 | 95.35 | 80.84 | 95.37 |
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## Training performance results
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2020-06-27 09:24:41 +02:00
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### Training performance: NVIDIA DGX A100 (8x A100 40G)
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Our results were obtained by running the applicable
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training scripts in the tensorflow-20.06-tf1-py3 NGC container
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on NVIDIA DGX A100 with (8x A100 40G) GPUs.
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Performance numbers (in images per second)
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were averaged over an entire training epoch.
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The specific training script that was run is documented
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in the corresponding model's README.
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The following table shows the training accuracy results of the
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three classification models side-by-side.
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| **arch** | **Mixed Precision XLA** | **TF32 XLA** | **Mixed Precision speedup** |
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|:-:|:-:|:-:|:-:|
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| resnet50 | 16400 img/s | 6300 img/s | 2.60x |
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| resnext101-32x4d | 8000 img/s | 2630 img/s | 3.05x |
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| se-resnext101-32x4d | 6930 img/s | 2400 img/s | 2.88x |
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2020-06-12 12:38:25 +02:00
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### Training performance: NVIDIA DGX-1 (8x V100 16G)
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Our results were obtained by running the applicable
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2020-06-27 09:24:41 +02:00
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training scripts in the tensorflow-20.06-tf1-py3 NGC container
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2020-06-12 12:38:25 +02:00
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on NVIDIA DGX-1 with (8x V100 16G) GPUs.
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Performance numbers (in images per second)
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were averaged over an entire training epoch.
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The specific training script that was run is documented
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in the corresponding model's README.
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The following table shows the training accuracy results of the
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three classification models side-by-side.
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2020-06-27 09:24:41 +02:00
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| **arch** | **Mixed Precision XLA** | **FP32 XLA** | **Mixed Precision speedup** |
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|:-:|:-:|:-:|:-:|
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| resnet50 | 9510 img/s | 3170 img/s | 3.00x |
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| resnext101-32x4d | 4160 img/s | 1210 img/s | 3.44x |
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| se-resnext101-32x4d | 3360 img/s | 1120 img/s | 3.00x |
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2020-06-12 12:38:25 +02:00
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## Release notes
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### Changelog
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2020-06-12 13:50:44 +02:00
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June 2020
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- ConvNets repo restructurization
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- Initial release of ResNext and SE-Resnext
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