Unofficial implementation of PatchCore anomaly detection

Related tags

Deep Learning PatchCore_anomaly_detection
Overview

PatchCore anomaly detection

Unofficial implementation of PatchCore(new SOTA) anomaly detection model

Original Paper : Towards Total Recall in Industrial Anomaly Detection (Jun 2021)
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, Peter Gehler

https://arxiv.org/abs/2106.08265
https://paperswithcode.com/sota/anomaly-detection-on-mvtec-ad

plot

notice(21/06/18) :
This code is not yet verified. Any feedback is appreciated.
updates(21/06/21) :

  • I used sklearn's SparseRandomProjection(ep=0.9) for random projection. I'm not confident with this.
  • I think exact value of "b nearest patch-features" is not presented in the paper. I just set 9. (args.n_neighbors)
  • In terms of NN search, author used "faiss". but not implemented in this code yet.
  • sample embeddings/carpet/embedding.pickle => coreset_sampling_ratio=0.001

updates(21/06/26) :

  • A critical issue related to "locally aware patch" raised and fixed. Score table is updated.

Usage

# install python 3.6, torch==1.8.1, torchvision==0.9.1
pip install -r requirements.txt

python train.py --phase train or test --dataset_path .../mvtec_anomaly_detection --category carpet --project_root_path path/to/save/results --coreset_sampling_ratio 0.01 --n_neighbors 9'

# for fast try just specify your dataset_path and run
python train.py --phase test --dataset_path .../mvtec_anomaly_detection --project_root_path ./

MVTecAD AUROC score (PatchCore-1%, mean of n trials)

Category Paper
(image-level)		 This code
(image-level)		 Paper
(pixel-level)		 This code
(pixel-level)
carpet 0.980 0.991(1) 0.989 0.989(1)
grid 0.986 0.975(1) 0.986 0.975(1)
leather 1.000 1.000(1) 0.993 0.991(1)
tile 0.994 0.994(1) 0.961 0.949(1)
wood 0.992 0.989(1) 0.951 0.936(1)
bottle 1.000 1.000(1) 0.985 0.981(1)
cable 0.993 0.995(1) 0.982 0.983(1)
capsule 0.980 0.976(1) 0.988 0.989(1)
hazelnut 1.000 1.000(1) 0.986 0.985(1)
metal nut 0.997 0.999(1) 0.984 0.984(1)
pill 0.970 0.959(1) 0.971 0.977(1)
screw 0.964 0.949(1) 0.992 0.977(1)
toothbrush 1.000 1.000(1) 0.985 0.986(1)
transistor 0.999 1.000(1) 0.949 0.972(1)
zipper 0.992 0.995(1) 0.988 0.984(1)
mean 0.990 0.988 0.980 0.977

Code Reference

kcenter algorithm :
https://github.com/google/active-learning
embedding concat function :
https://github.com/xiahaifeng1995/PaDiM-Anomaly-Detection-Localization-master

Comments
  • ValueError: cannot reshape array of size 1024 into shape (28,28)

    ValueError: cannot reshape array of size 1024 into shape (28,28)

    When I run the test phase of this project(mvtec/carpet)，I got this error:

    train.py", line 333, in test_step anomaly_map = score_patches[:,0].reshape((28,28)) ValueError: cannot reshape array of size 1024 into shape (28,28) Testing: 0%| | 0/117 [00:09<?, ?it/s] Any ideas? thank you !

    opened by xfby2016 6
  • Detection time is related to the training set size?

    Detection time is related to the training set size?

    Hi,I find that the detection time grows longer as the training set grows,Is that reasonable? The problem seems to related to this line of code, https://github.com/hcw-00/PatchCore_anomaly_detection/blob/5680042fd114abbfbef820edef1f61d566a128fd/train.py#L280 Or is that a problem?Thanks

    opened by xfby2016 5
  • train speed get more and more slowly

    train speed get more and more slowly

    Hi, I am trying to train the model in dataset, however I find that the speed is getting more and more slowly. I noticed that the embedding size is getting bigger, so whether we can change it? Thanks. image

    opened by XiaoPengZong 4
  • torch.cdist works faster than distance_matrix.

    torch.cdist works faster than distance_matrix.

    and it needs more small gpu-memory.

    There is a memory overload in torch.pow(x-y,p).sum() When I try with this code, I already reserve 8GB and try to allocate another 8GB.

    I have a RTX 3080 GPU with 10GB memory. And when I input big image (3,896,896), distance matrix needs 14.36GB but torch.cdist works well.

    It means we can use more large sampling rates by using torch.cdist function. I can run sampling rate = 0.01 with torch.cdist but I got CUDA out of memory with distance_matrix.

    Actual GPU memory usages with MVTec AD. https://github.com/hcw-00/PatchCore_anomaly_detection/issues/19#issuecomment-962706455

    opened by paining 3
  • Using layer4

    Using layer4

    Thanks for this great implementation. In the paper the authors claim in "Evaluation on other benchmarks":

    As the detection context is much closer to that of natural image data available in ImageNet and images are larger, we make use of deeper network feature maps at hierarchy levels 3 and 4, but otherwise do not perform any hyperparameter tuning for PatchCore.

    I tried to simply swap layer2 and 3 to 3 and 4 and had to adapt the reshaping later on. But the results were a complete failure. The heatmap is always blank.

    Has someone tried to use layer4 successfully?

    opened by stvogel 3
  • Memory Explosion

    Memory Explosion

    Hi I run the train.py under following env:

    1. Ubuntu 16.04, docker env: pytorch 1.8.1 with GPU support
    2. Machine Memory: 32G, swap area size: 12GB; GPU: GF RTX 2080 Ti 12 GB
    3. Num of epochs:50, batch_size:1, load_size: 256, input_size:224, with other parameters default/unchanged
    4. Trainning dataset consists of 150 non-defect images with 1300*2300 resolution
    5. Test dataset consists of 100 defect and non-defect images, with ground truth images where a pure black for non-defect images and write musk for the defect areas for defect images

    Problem: After epoch 11, the machine memory (not GPU memory) takes up to 40GB, and at epoch 15, the usage of machine memory would be over 50GB, then the system would kill the training process.

    Any ideas why this happen, and how to fix?

    opened by anan-dad 3
  • how test my own dataset without groundtrouth?

    how test my own dataset without groundtrouth?

    As described in title, the data from real world without GT_mask, but almost every algorithms need gt_mask to get roc_auc_score. Do you try to decide the threshold of anomaly score which higher than the threshold will be thought as anomaly ? Other than obtained the optim threshold from GT ? The problem confiused me all the time.

    opened by letmejoin 3
  • Parameter: coreset_sampling_ratio 0.01 happened error

    Parameter: coreset_sampling_ratio 0.01 happened error

    Hello, I have try to run your code by your sample cmd

    python train.py --phase train or test --dataset_path .../mvtec_anomaly_detection --category carpet --project_root_path path/to/save/results --coreset_sampling_ratio 0.01 --n_neighbors 9

    But I found coreset_sampling_ratio 0.01 will make error so I try use default parameters 0.001 and this is correctly work.

    Error code

    Error report is : Traceback (most recent call last): File "train.py", line 441, in <module> trainer.fit(model) File "/home/neaf-3090/.pyenv/versions/3.6.8/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 458, in fit self._run(model) File "/home/neaf-3090/.pyenv/versions/3.6.8/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 756, in _run self.dispatch() File "/home/neaf-3090/.pyenv/versions/3.6.8/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 797, in dispatch self.accelerator.start_training(self) File "/home/neaf-3090/.pyenv/versions/3.6.8/lib/python3.6/site-packages/pytorch_lightning/accelerators/accelerator.py", line 96, in start_training self.training_type_plugin.start_training(trainer) File "/home/neaf-3090/.pyenv/versions/3.6.8/lib/python3.6/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 144, in start_training self._results = trainer.run_stage() File "/home/neaf-3090/.pyenv/versions/3.6.8/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 807, in run_stage return self.run_train() File "/home/neaf-3090/.pyenv/versions/3.6.8/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 869, in run_train self.train_loop.run_training_epoch() File "/home/neaf-3090/.pyenv/versions/3.6.8/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 556, in run_training_epoch self.on_train_epoch_end(epoch_output) File "/home/neaf-3090/.pyenv/versions/3.6.8/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 596, in on_train_epoch_end training_epoch_end_output = model.training_epoch_end(processed_epoch_output) File "train.py", line 345, in training_epoch_end selected_idx = selector.select_batch(model=self.randomprojector, already_selected=[], N=int(total_embeddings.shape[0]*args.coreset_sampling_ratio)) ValueError: invalid literal for int() with base 10: '0.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.0

    Look like the problem is happened in line 345: selected_idx = selector.select_batch(model=self.randomprojector, already_selected=[], N=int(total_embeddings.shape[0]*args.coreset_sampling_ratio))

    System and python interpreter

    • Ubuntu 20
    • Python 3.8
    • pip 21.2.4
    • torch 1.9.0
    • torchvision 0.10

    Can you tell me why coreset_sampling_ratio set 0.01 will make this bug ?

    thx~

    opened by RozenAstrayChen 2
  • Trying to run the code with MVTec AD but: TypeError: 'NoneType' object is not iterable

    Trying to run the code with MVTec AD but: TypeError: 'NoneType' object is not iterable

    I tried to run the code with the carpet class of the MVTec AD, but the above error was yielded. I followed the installation process but, even so, this error appeared. Any idea why?

    opened by andreluizbvs 2
  • Possible error at final anomaly map

    Possible error at final anomaly map

    hey there, first of all thanks for the code!

    regarding the final anomaly map computed: shouldn't the finale rescaling of the scores be done to all patch features? and not only to the highest one? thus creating a different anomaly map for the whole image?

    any thoughts? thanks!

    opened by shakediel 2
  • Trivial use of AdaptiveAvgPool2d

    Trivial use of AdaptiveAvgPool2d

    https://github.com/hcw-00/PatchCore_anomaly_detection/blob/ded157b8546bd71338b89a47d6ee5a872f382b14/train.py#L295

    AdaptiveAvgPool is applied to each feature map on the same scale due to feature[0].shape[-2:] This means that nothing happens to the feature maps.

    If you want to rescale the second feature maps to the first, I guess you could use features[0].shape[-2:]

    Furthermore, even if you would use the suggestion above, you still don't apply any aggregation function f_agg to the first feature maps (as described in the paper). Shouldn't you use AvgPool2d in order to actually aggregate information from nearby patches?

    opened by rvorias 2
  • What is training on patchCore? (I think that patchCore does not have training phase)

    What is training on patchCore? (I think that patchCore does not have training phase)

    Hello Sir.

    I have interesting anomaly detection. I found your code in paperwithcode, I want to try to test your code.

    I think that patchCore does not have training-phase (Neural Networks) as PaDiM. But your code has training-phase.

    What is this??

    Thanks, Edward Cho.

    opened by edwardcho 3
  • train error:ValueError: invalid literal for int() with base 10:

    train error:ValueError: invalid literal for int() with base 10:

    Epoch 0: 100%|█████████████████████████████████████████████████████Epoch 0: 100%|████████████████████████████████████████████████████████████████| 62/62 [00:20<00:00, 2.98it/s, v_num=6]Traceback (most recent call last): File "train.py", line 440, in trainer.fit(model) File "F:\ProgramData\Anaconda3\envs\patchcore\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 696, in fit self._call_and_handle_interrupt( File "F:\ProgramData\Anaconda3\envs\patchcore\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 650, in _call_and_handle_interrupt return trainer_fn(*args, **kwargs) File "F:\ProgramData\Anaconda3\envs\patchcore\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 737, in _fit_impl results = self._run(model, ckpt_path=self.ckpt_path) File "F:\ProgramData\Anaconda3\envs\patchcore\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1168, in _run results = self._run_stage() File "F:\ProgramData\Anaconda3\envs\patchcore\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1254, in _run_stage return self._run_train() File "F:\ProgramData\Anaconda3\envs\patchcore\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1285, in _run_train self.fit_loop.run() File "F:\ProgramData\Anaconda3\envs\patchcore\lib\site-packages\pytorch_lightning\loops\loop.py", line 201, in run self.on_advance_end() File "F:\ProgramData\Anaconda3\envs\patchcore\lib\site-packages\pytorch_lightning\loops\fit_loop.py", line 286, in on_advance_end epoch_end_outputs = self.trainer._call_lightning_module_hook("training_epoch_end", epoch_end_outputs) File "F:\ProgramData\Anaconda3\envs\patchcore\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1552, in _call_lightning_module_hook output = fn(*args, **kwargs) File "train.py", line 354, in training_epoch_end selected_idx = selector.select_batch(model=self.randomprojector, already_selected=[], N=int(total_embeddings.shape[0]*args.coreset_sampling_ratio)) ValueError: invalid literal for int() with base 10: '0.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.0

    opened by HBing110 2
  • num_samples should be a positive integer value, but got num_samples=0

    num_samples should be a positive integer value, but got num_samples=0

    I ran the code and got many errors but handled to solve them somehow! here is a problem that I have. I only want to train the model on the bottle dataset and it is already in the dataset address. the original dataset has broken_large, broken_small, contaminated, and good but I do not know how to feed those 4 groups as a train/test dataset.

    I also have got an error while training only on good images. I will appreciate it if you could help me

    $ python train.py --phase train --dataset_path C:\\projects\\interview\\mvtec --category bottle --project_root_path C:\projects\interview\PatchCore_anomaly_detection\results --coreset_sampling_ratio 0.01 --n_neighbors 9 C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\torchvision\models\detection\anchor_utils.py:63: UserWarning: Failed to initialize NumPy: module compiled against API version 0x10 but this version of numpy is 0xf (Triggered internally at ..\torch\csrc\utils\tensor_numpy.cpp:68.) device: torch.device = torch.device("cpu"), GPU available: False, used: False TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs Using cache found in C:\Users\user/.cache\torch\hub\pytorch_vision_v0.9.0 C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\torchvision\models\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead. warnings.warn( C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\torchvision\models\_utils.py:223: UserWarning: Arguments other than a weight enum orNonefor 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passingweights=Wide_ResNet50_2_Weights.IMAGENET1K_V1. You can also useweights=Wide_ResNet50_2_Weights.DEFAULTto get the most up-to-date weights. warnings.warn(msg) C:\projects\interview\PatchCore_anomaly_detection_2\train.py:259: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead. transforms.Resize((args.load_size, args.load_size), Image.ANTIALIAS), C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\torchvision\transforms\transforms.py:332: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum. warnings.warn( C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\optimizers.py:37: UserWarning:LightningModule.configure_optimizersreturnedNone`, this fit will run with no optimizer rank_zero_warn(

    | Name | Type | Params

    0 | model | ResNet | 68.9 M 1 | criterion | MSELoss | 0 2 | inv_normalize | Normalize | 0

    0 Trainable params 68.9 M Non-trainable params 68.9 M Total params 275.533 Total estimated model params size (MB) Traceback (most recent call last): File "C:\projects\interview\PatchCore_anomaly_detection_2\train.py", line 437, in trainer.fit(model) File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\trainer.py", line 740, in fit self._call_and_handle_interrupt( File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\trainer.py", line 685, in _call_and_handle_interrupt return trainer_fn(*args, **kwargs) File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\trainer.py", line 777, in _fit_impl self._run(model, ckpt_path=ckpt_path) File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\trainer.py", line 1199, in _run self._dispatch() File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\trainer.py", line 1279, in _dispatch self.training_type_plugin.start_training(self) File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\plugins\training_type\training_type_plugin.py", line 202, in start_training self._results = trainer.run_stage() File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\trainer.py", line 1289, in run_stage return self._run_train() File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\trainer.py", line 1319, in _run_train self.fit_loop.run() File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\loops\base.py", line 140, in run self.on_run_start(*args, **kwargs) File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\loops\fit_loop.py", line 197, in on_run_start self.trainer.reset_train_val_dataloaders(self.trainer.lightning_module) File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\data_loading.py", line 595, in reset_train_val_dataloaders self.reset_train_dataloader(model=model) File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\data_loading.py", line 365, in reset_train_dataloader self.train_dataloader = self.request_dataloader(RunningStage.TRAINING, model=model) File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\data_loading.py", line 611, in request_dataloader dataloader = source.dataloader() File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\connectors\data_connector.py", line 296, in dataloader return self.instance.trainer.call_hook(self.name, pl_module=self.instance) File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\trainer.py", line 1501, in call_hook output = model_fx(*args, **kwargs) File "C:\projects\interview\PatchCore_anomaly_detection_2\train.py", line 304, in train_dataloader train_loader = DataLoader(image_datasets, batch_size=args.batch_size, shuffle=True, num_workers=0) #, pin_memory=True) File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\torch\utils\data\dataloader.py", line 347, in init sampler = RandomSampler(dataset, generator=generator) # type: ignore[arg-type] File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\torch\utils\data\sampler.py", line 107, in init raise ValueError("num_samples should be a positive integer " ValueError: num_samples should be a positive integer value, but got num_samples=0 `

    opened by MSadriAghdam 0
  • wondering if the coreset sampling is only random sampling now

    wondering if the coreset sampling is only random sampling now

    If I understand correctly, the kCenterGreedy.select_batch_ function is merely doing random sampling now. The main reason is that self.already_selected is not updated within the range(N) loop, therefore line 'ind = np.random.choice(np.arange(self.n_obs))' is always executed. To fix it, put self.already_selected = new_batch into the range(N) loop.

    Please point it out if I misunderstood anything!

    for _ in range(N): if not self.already_selected: # Initialize centers with a randomly selected datapoint ind = np.random.choice(np.arange(self.n_obs)) else: ind = np.argmax(self.min_distances) # New examples should not be in already selected since those points # should have min_distance of zero to a cluster center. assert ind not in already_selected

      self.update_distances([ind], only_new=True, reset_dist=False)
  new_batch.append(ind)
  print('Maximum distance from cluster centers is %0.2f' % max(self.min_distances))

  self.already_selected = new_batch#already_selected
    opened by YuchenKid 0
  • using pip install for faiss is not recommanded

    using pip install for faiss is not recommanded

    I have tried pip version of faiss for my own experiments, faiss-cpu and faiss-gpu, and I found my results are unstable, after searching on google, https://github.com/facebookresearch/faiss/issues/2273 pip is not a supported way of installing in my user experience, I found that faiss(pip version) is very unstable, and also hard to debug, sometimes my program hangs when running faiss, anomalib provides an alternative way to do nearest neighbor search.

    opened by ghost 0
Owner
Changwoo Ha
ML & DL
Changwoo Ha
GitHub
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A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning", CIKM-21

ANEMONE A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning", CIKM-21 Dependencies python==3.6.1 dgl==

Graph Analysis & Deep Learning Laboratory, GRAND 30 Dec 14, 2022
Official Implementation for the "An Empirical Investigation of 3D Anomaly Detection and Segmentation" paper.

An Empirical Investigation of 3D Anomaly Detection and Segmentation Project | Paper Official PyTorch Implementation for the "An Empirical Investigatio

Eliahu Horwitz 55 Dec 14, 2022
Paper list of log-based anomaly detection

Paper list of log-based anomaly detection

Weibin Meng 411 Dec 5, 2022
Demo project for real time anomaly detection using kafka and python

kafkaml-anomaly-detection Project for real time anomaly detection using kafka and python It's assumed that zookeeper and kafka are running in the loca

Rodrigo Arenas 36 Dec 12, 2022
MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift

MemStream Implementation of MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift . Siddharth Bhatia, Arjit Jain, Shivi

Stream-AD 61 Dec 2, 2022
USAD - UnSupervised Anomaly Detection on multivariate time series

USAD - UnSupervised Anomaly Detection on multivariate time series Scripts and utility programs for implementing the USAD architecture. Implementation

null 116 Jan 4, 2023
LogDeep is an open source deeplearning-based log analysis toolkit for automated anomaly detection.

LogDeep is an open source deeplearning-based log analysis toolkit for automated anomaly detection.

donglee 279 Dec 13, 2022
Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks This is a Python3 / Pytorch implementation of TadGAN paper. The associated

Arun 92 Dec 3, 2022
Industrial knn-based anomaly detection for images. Visit streamlit link to check out the demo.

Industrial KNN-based Anomaly Detection ⭐ Now has streamlit support! ⭐ Run $ streamlit run streamlit_app.py This repo aims to reproduce the results of

aventau 102 Dec 26, 2022
Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"

CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows WACV 2022 preprint:https://arxiv.org/abs/2107.1

Denis 156 Dec 28, 2022
This is the accompanying toolbox for the paper "A Survey on GANs for Anomaly Detection"

Anomaly detection using GANs.

Zuru Tech 77 Nov 30, 2022
Deep Anomaly Detection with Outlier Exposure (ICLR 2019)

Outlier Exposure This repository contains the essential code for the paper Deep Anomaly Detection with Outlier Exposure (ICLR 2019). Requires Python 3

Dan Hendrycks 464 Dec 27, 2022
Patch SVDD for Image anomaly detection

Patch SVDD Patch SVDD for Image anomaly detection. Paper: https://arxiv.org/abs/2006.16067 (published in ACCV 2020). Original Code : https://github.co

Hong-Jeongmin 0 Dec 3, 2021
Anomaly Detection Based on Hierarchical Clustering of Mobile Robot Data

We proposed a new approach to detect anomalies of mobile robot data. We investigate each data seperately with two clustering method hierarchical and k-means. There are two sub-method that we used for produce an anomaly score. Then, we merge these two score and produce merged anomaly score as a result.

Zekeriyya Demirci 1 Jan 9, 2022