iicd/autodl-projects
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

update README

Browse Source
This commit is contained in:
Xuanyi Dong 2019-04-10 14:35:20 +08:00
parent 1348d17989
commit 17b615e792
1 changed files with 3 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
README.md
View File

@ -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
<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.
### 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},