https://github.com/sovrasov/lightweight-robust-models
Scripts for training adversarially robust classification models
https://github.com/sovrasov/lightweight-robust-models
adversarial-attacks adversarial-learning python3 pytorch
Last synced: 18 days ago
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Scripts for training adversarially robust classification models
- Host: GitHub
- URL: https://github.com/sovrasov/lightweight-robust-models
- Owner: sovrasov
- License: mit
- Created: 2020-09-21T17:09:55.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-09-01T12:43:32.000Z (almost 4 years ago)
- Last Synced: 2025-10-10T08:12:28.794Z (8 months ago)
- Topics: adversarial-attacks, adversarial-learning, python3, pytorch
- Language: Python
- Homepage:
- Size: 123 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Scripts for training adversarially robust classification models
This set of scripts allows train and evaluate robust to [PGD](https://arxiv.org/abs/1706.06083) attack classification
models on ImageNet dataset. Implementations of models are taken from the [pytorchcv](https://github.com/osmr/imgclsmob/tree/master/pytorch/pytorchcv) library,
which means robust weights can be substituted to existing scripts that rely on pytorchcv.
## Trained models
All the models are trained on adversarial samples obtained after 3 iterations of PGD with step=2/3*eps.
Model | Input Resolution | Params(M) | MACs(G) | eps | dist | Top-1 accuracy | Top-5 accuracy | Top-1 adv accuracy | Top-5 adv accuracy
--- |--- |--- |--- |--- |--- |--- |--- |--- |---
[MobilenetV2 1x](https://drive.google.com/file/d/1WCRjp9Q1oIuRpjmu9hLuE8uf9pLV2LMN/view?usp=sharing) |224x224 | 3.4 | 0.3 | 0.02| l2 | 72.16 | 90.62 | 71.72 | 90.40
[MobilenetV2 1x](https://drive.google.com/file/d/1O82imwnSBfiaLRFs361jXWmgwkjWwYGv/view?usp=sharing) |224x224 | 3.4 | 0.3 | 0.05| l2 | 72.12 | 90.34 | 71.11 | 89.84
[MobilenetV2 1x](https://drive.google.com/file/d/1Cz89u3J-0yrx8v8LGP98c4xQw3Dz5sfe/view?usp=sharing) |224x224 | 3.4 | 0.3 | 0.3 | l2 | 71.38 | 89.8 | 68.79 | 88.50
An example of training command:
```bash
python train.py \
-a mobilenetv2_w1 \
-b 256 \
-d $IMAGENET_FOLDER \
--epochs 150 \
--lr-decay cos \
--lr 0.05 \
--wd 4e-5 \
-c ./snapshots \
--input-size 224 \
--adv-eps 0.3 \
--euclidean \
-j 40
```
An example of an evaluation command:
```bash
python3 train.py \
-a mobilenetv2_w1 \
-d $IMAGENET_FOLDER \
-b 128 \
--weight ./snapshots/model_best.pth.tar \
--evaluate \
--input-size 224 \
-j 8 \
--adv-eps 0.3 \
--euclidean
```