diff --git a/README.md b/README.md index 8f4a81b..1673f77 100644 --- a/README.md +++ b/README.md @@ -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/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 @@ -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). ### Experimental Results - + Figure 2. Top-1 and top-5 errors on ImageNet. ### Citation +If you find that this project (GDAS) helps your research, please cite the paper: ``` @inproceedings{dong2019search, title={Searching for A Robust Neural Architecture in Four GPU Hours},