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update README

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Xuanyi Dong
2019-04-10 14:35:20 +08:00
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@@ -32,6 +32,7 @@ Evaluate a trained CNN model
``` ```
CUDA_VISIBLE_DEVICES=0 python ./exps-cnn/evaluate.py --data_path $TORCH_HOME/cifar.python --checkpoint ${checkpoint-path} CUDA_VISIBLE_DEVICES=0 python ./exps-cnn/evaluate.py --data_path $TORCH_HOME/cifar.python --checkpoint ${checkpoint-path}
CUDA_VISIBLE_DEVICES=0 python ./exps-cnn/evaluate.py --data_path $TORCH_HOME/ILSVRC2012 --checkpoint ${checkpoint-path} CUDA_VISIBLE_DEVICES=0 python ./exps-cnn/evaluate.py --data_path $TORCH_HOME/ILSVRC2012 --checkpoint ${checkpoint-path}
CUDA_VISIBLE_DEVICES=0 python ./exps-cnn/evaluate.py --data_path $TORCH_HOME/ILSVRC2012 --checkpoint GDAS-V1-C50-N14-ImageNet.pth
``` ```
Train the searched RNN Train the searched RNN
@@ -48,10 +49,11 @@ CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-WT2.sh GDAS
Some training logs can be found in `./data/logs/`, and some pre-trained models can be found in [Google Driver](https://drive.google.com/open?id=1Ofhc49xC1PLIX4O708gJZ1ugzz4td_RJ). Some training logs can be found in `./data/logs/`, and some pre-trained models can be found in [Google Driver](https://drive.google.com/open?id=1Ofhc49xC1PLIX4O708gJZ1ugzz4td_RJ).
### Experimental Results ### Experimental Results
<img src="data/imagenet-results.png" width="600"> <img src="data/imagenet-results.png" width="700">
Figure 2. Top-1 and top-5 errors on ImageNet. Figure 2. Top-1 and top-5 errors on ImageNet.
### Citation ### Citation
If you find that this project (GDAS) helps your research, please cite the paper:
``` ```
@inproceedings{dong2019search, @inproceedings{dong2019search,
title={Searching for A Robust Neural Architecture in Four GPU Hours}, title={Searching for A Robust Neural Architecture in Four GPU Hours},