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-# CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
+# Masterarbeit: Anomalie-Detektion in Zellbildern zur Anwendung der Leukaemieerkennung
-Official PyTorch implementation of
-["**CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances**"](
-https://arxiv.org/abs/2007.08176) (NeurIPS 2020) by
-[Jihoon Tack*](https://jihoontack.github.io),
-[Sangwoo Mo*](https://sites.google.com/view/sangwoomo),
-[Jongheon Jeong](https://sites.google.com/view/jongheonj),
-and [Jinwoo Shin](http://alinlab.kaist.ac.kr/shin.html).
+CSI Methode mit Codeanpassungen bzw. Ergaenzungen.
-
- 
-
+**Offizieller Release von Tack et al.:
+https://github.com/alinlab/CSI#environments**
-## 1. Requirements
-### Environments
-Currently, requires following packages
-- python 3.6+
-- torch 1.4+
-- torchvision 0.5+
-- CUDA 10.1+
-- scikit-learn 0.22+
-- tensorboard 2.0+
-- [torchlars](https://github.com/kakaobrain/torchlars) == 0.1.2
-- [pytorch-gradual-warmup-lr](https://github.com/ildoonet/pytorch-gradual-warmup-lr) packages
-- [apex](https://github.com/NVIDIA/apex) == 0.1
-- [diffdist](https://github.com/ag14774/diffdist) == 0.1
-
-### Datasets
-For CIFAR, please download the following datasets to `~/data`.
-* [LSUN_resize](https://www.dropbox.com/s/moqh2wh8696c3yl/LSUN_resize.tar.gz),
-[ImageNet_resize](https://www.dropbox.com/s/kp3my3412u5k9rl/Imagenet_resize.tar.gz)
-* [LSUN_fix](https://drive.google.com/file/d/1KVWj9xpHfVwGcErH5huVujk9snhEGOxE/view?usp=sharing),
-[ImageNet_fix](https://drive.google.com/file/d/1sO_-noq10mmziB1ECDyNhD5T4u5otyKA/view?usp=sharing)
-
-For ImageNet-30, please download the following datasets to `~/data`.
-* [ImageNet-30-train](https://drive.google.com/file/d/1B5c39Fc3haOPzlehzmpTLz6xLtGyKEy4/view),
-[ImageNet-30-test](https://drive.google.com/file/d/13xzVuQMEhSnBRZr-YaaO08coLU2dxAUq/view)
-* [CUB-200](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html),
-[Stanford Dogs](http://vision.stanford.edu/aditya86/ImageNetDogs/),
-[Oxford Pets](https://www.robots.ox.ac.uk/~vgg/data/pets/),
-[Oxford flowers](https://www.robots.ox.ac.uk/~vgg/data/flowers/),
-[Food-101](https://www.kaggle.com/dansbecker/food-101),
-[Places-365](http://data.csail.mit.edu/places/places365/val_256.tar),
-[Caltech-256](https://www.kaggle.com/jessicali9530/caltech256),
-[DTD](https://www.robots.ox.ac.uk/~vgg/data/dtd/)
-
-For Food-101, remove hotdog class to avoid overlap.
-
-## 2. Training
-Currently, all code examples are assuming distributed launch with 4 multi GPUs.
-To run the code with single GPU, remove `-m torch.distributed.launch --nproc_per_node=4`.
-
-### Unlabeled one-class & multi-class
-To train unlabeled one-class & multi-class models in the paper, run this command:
-
-```train
-CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 train.py --dataset --model --mode simclr_CSI --shift_trans_type rotation --batch_size 32 --one_class_idx
-```
-
-> Option --one_class_idx denotes the in-distribution of one-class training.
-> For multi-class training, set --one_class_idx as None.
-> To run SimCLR simply change --mode to simclr.
-> Total batch size should be 512 = 4 (GPU) * 32 (--batch_size option) * 4 (cardinality of shifted transformation set).
-
-### Labeled multi-class
-To train labeled multi-class model (confidence calibrated classifier) in the paper, run this command:
-
-```train
-# Representation train
-CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 train.py --dataset --model --mode sup_simclr_CSI --shift_trans_type rotation --batch_size 32 --epoch 700
-# Linear layer train
-python train.py --mode sup_CSI_linear --dataset --model --batch_size 32 --epoch 100 --shift_trans_type rotation --load_path
-```
-
-> To run SupCLR simply change --mode to sup_simclr, sup_linear for representation training and linear layer training respectively.
-> Total batch size should be same as above. Currently only supports rotation for shifted transformation.
-
-## 3. Evaluation
-
-We provide the checkpoint of the CSI pre-trained model. Download the checkpoint from the following link:
-- One-class CIFAR-10: [ResNet-18](https://drive.google.com/drive/folders/1z02i0G_lzrZe0NwpH-tnjpO8pYHV7mE9?usp=sharing)
-- Unlabeled (multi-class) CIFAR-10: [ResNet-18](https://drive.google.com/file/d/1yUq6Si6hWaMa1uYyLDHk0A4BrPIa8ECV/view?usp=sharing)
-- Unlabeled (multi-class) ImageNet-30: [ResNet-18](https://drive.google.com/file/d/1KucQWSik8RyoJgU-fz8XLmCWhvMOP7fT/view?usp=sharing)
-- Labeled (multi-class) CIFAR-10: [ResNet-18](https://drive.google.com/file/d/1rW2-0MJEzPHLb_PAW-LvCivHt-TkDpRO/view?usp=sharing)
-
-### Unlabeled one-class & multi-class
-To evaluate my model on unlabeled one-class & multi-class out-of-distribution (OOD) detection setting, run this command:
-
-```eval
-python eval.py --mode ood_pre --dataset --model --ood_score CSI --shift_trans_type rotation --print_score --ood_samples 10 --resize_factor 0.54 --resize_fix --one_class_idx --load_path
-```
-
-> Option --one_class_idx denotes the in-distribution of one-class evaluation.
-> For multi-class evaluation, set --one_class_idx as None.
-> The resize_factor & resize fix option fix the cropping size of RandomResizedCrop().
-> For SimCLR evaluation, change --ood_score to simclr.
-
-### Labeled multi-class
-To evaluate my model on labeled multi-class accuracy, ECE, OOD detection setting, run this command:
-
-```eval
-# OOD AUROC
-python eval.py --mode ood --ood_score baseline_marginalized --print_score --dataset --model --shift_trans_type rotation --load_path
-# Accuray & ECE
-python eval.py --mode test_marginalized_acc --dataset --model --shift_trans_type rotation --load_path
-```
-
-> This option is for marginalized inference.
-> For single inference (also used for SupCLR) change --ood_score baseline in first command,
-> and --mode test_acc in second command.
-
-## 4. Results
-
-Our model achieves the following performance on:
-
-### One-Class Out-of-Distribution Detection
-
-| Method | Dataset | AUROC (Mean) |
-| --------------|------------------ | --------------|
-| SimCLR | CIFAR-10-OC | 87.9% |
-| Rot+Trans | CIFAR-10-OC | 90.0% |
-| CSI (ours) | CIFAR-10-OC | 94.3% |
-
-We only show CIFAR-10 one-class result in this repo. For other setting, please see our paper.
-
-### Unlabeled Multi-Class Out-of-Distribution Detection
-
-| Method | Dataset | OOD Dataset | AUROC (Mean) |
-| --------------|------------------ |---------------|--------------|
-| Rot+Trans | CIFAR-10 | CIFAR-100 | 82.5% |
-| CSI (ours) | CIFAR-10 | CIFAR-100 | 89.3% |
-
-We only show CIFAR-10 to CIFAR-100 OOD detection result in this repo. For other OOD dataset results, see our paper.
-
-### Labeled Multi-Class Result
-
-| Method | Dataset | OOD Dataset | Acc | ECE | AUROC (Mean) |
-| ---------------- |------------------ |---------------|-------|-------|--------------|
-| SupCLR | CIFAR-10 | CIFAR-100 | 93.9% | 5.54% | 88.3% |
-| CSI (ours) | CIFAR-10 | CIFAR-100 | 94.8% | 4.24% | 90.6% |
-| CSI-ensem (ours) | CIFAR-10 | CIFAR-100 | 96.0% | 3.64% | 92.3% |
-
-We only show CIFAR-10 with CIFAR-100 as OOD in this repo. For other dataset results, please see our paper.
-
-## 5. New OOD dataset
-
-
- 
-
-
-We find that current benchmark datasets for OOD detection, are visually far from in-distribution datasets (e.g. CIFAR).
-
-To address this issue, we provide new datasets for OOD detection evaluation:
-[LSUN_fix](https://drive.google.com/file/d/1KVWj9xpHfVwGcErH5huVujk9snhEGOxE/view?usp=sharing),
-[ImageNet_fix](https://drive.google.com/file/d/1sO_-noq10mmziB1ECDyNhD5T4u5otyKA/view?usp=sharing).
-See the above figure for the visualization of current benchmark and our dataset.
-
-To generate OOD datasets, run the following codes inside the `./datasets` folder:
-
-```OOD dataset generation
-# ImageNet FIX generation code
-python imagenet_fix_preprocess.py
-# LSUN FIX generation code
-python lsun_fix_preprocess.py
-```
-
-## Citation
-```
-@inproceedings{tack2020csi,
- title={CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances},
- author={Jihoon Tack and Sangwoo Mo and Jongheon Jeong and Jinwoo Shin},
- booktitle={Advances in Neural Information Processing Systems},
- year={2020}
-}
-```
+Zum trainieren von Modellen: train.ipynb
+Zum evaluieren von Modellen: eval.ipynb
+Bereits trainierte Modelle in: ../logs/
\ No newline at end of file