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 # Example Algorithms
 
 This folder contains a few simple example OoD algorithms for the _Medical Out-of-Distribution Analysis Challenge_.
 
 ### Quickstart
 
 Install python requirements:
 
 ```
-pip install -r requirements_alogs.txt
+pip install -r requirements_algos.txt
 ```
 
 And use the data/preprocess.py script to preprocess the data (you may want to use more sophisticated preprocessing for your own submission):
 
 ```
 python data/preprocess.py -i input_folder -o output_folder
 ```
 
 ### Run the example algorithms
 
 Basically all the algorithms in the algorithm folder are ready-to-run, or can be used as a starting point for your own algorithms. All algorithms take the same basic command_line arguments:
 
 - -r [--run], Which part of the algorithm pipeline you want to run ("train", "predict", "test", "all").
 - -d [--data-dir], The directory containing the preprocessed training data.
 - -o [--log-dir], The directory to which you want the logs to be stored in.
 - -t [--test-dir], The directory containing the test data (requires a folder with the same name plus the suffix '\_label').
 - -m [--mode], pixel-level or sample-level algorithm ('pixel', 'sample').
 - --logger, if you want to use a logger, can either use a visdom server (running on port 8080) or tensorboard ("visdom", "tensorboard").
 
 For more arguments checkout the python files.
 
 The example Algorithms includes:
 
 #### 2D Autoencoder (2d_ae.py)
 
 A simple 2d autoencoder which uses the reconstruction error as OoD score:
 
 ```
 python algorithms/ae_2d.py -r all -o output_dir -t /data/mood/brain/toy  --mode pixel --logger visdom -d /data/mood/brain/train_preprocessed
 ```
 
 #### 3D Autoencoder (2d_ae.py)
 
 A simple 3d autoencoder which uses the reconstruction error as OoD score:
 
 ```
 python algorithms/ae_3d.py -r all -o output_dir -t /data/mood/brain/toy  --mode pixel --logger visdom -d /data/mood/brain/train_preprocessed
 ```
 
 #### ceVAE (ce_vae.py)
 
 A simple context-encoding Variational Autoencoder. Can be used as only a VAE or CE as well.
 
 ```
 python algorithms/ce_vae.py -r all -o output_dir -t /data/mood/brain/toy  --mode pixel --logger visdom -d /data/mood/brain/train_preprocessed --ce-factor 0.5 --score-mode combi
 ```
 
 With additional arguments:
 
 - --ce-factor, determins the 'mixing' between VAE and CE (0.0=VAE only, 1.0=CE only).
 - --score-mode, How to determine the OoD score ("rec", "grad", "combi").
 
 #### fAnoGAN (f_ano_gan.py)
 
 A fAnoGAN algorithm build on top of improved Wasserstein GANs. Can use be used as AnoGAN only (without the encoding).
 
 ```
 python algorithms/f_ano_gan.py -r all -o output_dir -t /data/mood/brain/toy  --mode pixel --logger visdom -d /data/mood/brain/train_preprocessed --use-encoder
 ```
 
 With argument:
 
 - --use-encoder/--no-encoder, to determine whether to train an additional encoder (fAnoGAN) to reconstruct the image or use no encoder and use backpropagation for reconstruction.
 
 ### More Code
 
 While the _example_algos_ code is ready to run there are a lot of excellent code repositories which can also be checked out. Some of the Include:
 
 - <https://github.com/StefanDenn3r/Unsupervised_Anomaly_Detection_Brain_MRI> , if you use tensorflow.
 - <https://github.com/AntixK/PyTorch-VAE>, for more VAE variants.
 - <https://github.com/hoya012/awesome-anomaly-detection>, for more pointers/ great algorimths.
 - <https://github.com/shubhomoydas/ad_examples> , <https://github.com/yzhao062/pyod> , collections of basic, easy-to-use algorithms.
 - <https://github.com/tSchlegl/f-AnoGAN>, the original f-AnoGAN implementation.
 - <https://github.com/houssamzenati/Efficient-GAN-Anomaly-Detection>
 - <https://github.com/izikgo/AnomalyDetectionTransformations>
 - <https://github.com/ashafaei/OD-test>
 
 And as always, _Papers With Code_:
 
 - [Anomaly Detection](https://paperswithcode.com/task/anomaly-detection/)
 - [Out-of-Distribution Detection](https://paperswithcode.com/task/out-of-distribution-detection/)
 - [Outlier Detection](https://paperswithcode.com/task/outlier-detection/)
 - [Density Estimation](https://paperswithcode.com/task/density-estimation/)
 
 Good Luck and Have Fun! =)