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Add default training parameters to classification refrence README #1998

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Mar 20, 2020
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Add default training parameters to classification refrence README #1998

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add default parameters to README
pmeier Mar 20, 2020
4c72082
fix vgg_*_bn
pmeier Mar 20, 2020
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26 changes: 25 additions & 1 deletion references/classification/README.md
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Expand Up @@ -4,7 +4,31 @@ This folder contains reference training scripts for image classification.
They serve as a log of how to train specific models, as provide baseline
training and evaluation scripts to quickly bootstrap research.

Except otherwise noted, all models have been trained on 8x V100 GPUs.
Except otherwise noted, all models have been trained on 8x V100 GPUs with
the following parameters:

| Parameter | value |
| ------------------------ | ------ |
| `--batch_size` | `32` |
| `--epochs` | `90` |
| `--lr` | `0.1` |
| `--momentum` | `0.9` |
| `--wd`, `--weight-decay` | `1e-4` |
| `--lr-step-size` | `30` |
| `--lr-gamma` | `0.1` |

### AlexNet and VGG

Since `AlexNet` and the original `VGG` architectures do not include batch
normalization, the default initial learning rate `--lr 0.1` is to high.

```
python main.py --model $MODEL --lr 1e-2
```

Here `$MODEL` is one of `alexnet`, `vgg11`, `vgg13`, `vgg16` or `vgg19`. Note
that `vgg11_bn`, `vgg13_bn`, `vgg16_bn`, and `vgg19_bn` include batch
normalization and thus are trained with the default parameters.

### ResNext-50 32x4d
```
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