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Anomalib with custom data setup(日本語つき)
Anomalib
Anomalib is a deep learning library that aims to collect state-of-the-art anomaly detection algorithms for benchmarking on both public and private datasets.
Scope
- This page is described troubleshooting of anomalib setup
- introduce my config file
- reference for my colleague.
- But definitely overlook this page, haha.
- That's why I decided share this information with you.
Summary of Anomalib
My environment is Anaconda
- If you will setup other, please refer to official web tutorial.
yes | conda create -n anomalib_env python=3.8
conda activate anomalib_env
git clone https://github.com/openvinotoolkit/anomalib.git
cd anomalib
pip install -e .
Custom dataset using
Try MVTEC dataset
-
For my convenience and learning anomalib training, implemented this sample.
-
dataset is saved "datasets"folder
- It is very huge data. If you don't need to use for your target, remove is better.
Anomaly Detection on MVTec AD
- I can see the anomalib detection accuracy!
Troubleshooting for custom data
- saved original data in the "datasets" folder
- almost similar folder construct with MVTEC data.
- modifying config.yaml, which I want to utilize
trouble examples
omegaconf.errors.ConfigAttributeError: ?????dict
- In this case we should check modified or created config.yaml parameter is correct.
- For my experirence, almost wrong data such a path, folder name and insufficient parameter.
insufficient parameter in dataset group.
normalization: imagenet # data distribution to which the images will be normalized: [none, imagenet]
- using wrong model name? backbone is correct for your target?
- Duplicated parameter also cause of failure.
model:
name: padim
backbone: wide_resnet50_2 #resnet18
wrong parameter example
- In this case normalization: value is wrong for me.
- Heat map become noise map....
correct parameter example
config.yaml for the custom dataset example
Added Patchcore config sample 23th Jul
Discussion