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Official implementation for masked contrastive learning for anomaly detection.(IJCAI-21)

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# MCL on (CIFAR-10, CIFAR-100) 
Official code for MCL. ([Masked Contrastive Learning for Anomaly Detection](https://arxiv.org/abs/2105.08793) IJCAI-2021)
This code includes SEI eval on CIFAR-100. (AUROC around 94.)
Some codes are from [SimCLR-CIFAR10](https://github.com/wangxin0716/SimCLR-CIFAR10).

## Dependencies
* pytorch >=1.2
* torchvision >=0.4.0
* hydra >=0.11.3
* tqdm >=4.45.0

### Install Hydra
[Hydra](https://hydra.cc/docs/next/intro/#installation) is a python framework to manage the hyperparameters during
 training and evaluation. Install with:
 
 ``pip install hydra-core --upgrade``

## Usage

Dataset download and preprocess (4-way rotations augmented dataset with rotation label)
``python utils/data_preprocess.py``

Train MCL:
``python mcl_main.py``

Use the following prefix to train MCL with single GPU :
``CUDA_VISIBLE_DEVICES="GPU_number"``

All the hyperparameters are available in ``mcl_config.yml``, 
which could be overrided from the command line.

## Evaluate trained model.

Download MCL [trained model](https://www.dropbox.com/s/hwag0bp6e6cbmab/epoch_800.pt?dl=0).

place trained model (``epoch_800.pt``) in  following directory.

``./logs/model_name(default : MCL)/ckpt/epoch_800.pt``

To evaluate pretrained model, set evaluate flag to True and load_epoch to 800 in ``mcl_config.yml``.

Then run ``python mcl_main.py``.

Experimental results are stored in ``./logs/model_name(default : MCL)/SEI performance_summary.txt`` file.

## Notes

Current version of SEI is not optimized enough, thus it might be slow.

MCL

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