Awesome
<p align="left"> <img src=assets/logo.svg width="70%" /> </p>
PyTorch re-implementation of Reconstruction by Inpainting for Visual Anomaly Detection
<br>1. AUROC Scores
| category | Paper | My Implementation |
|---|---|---|
| zipper | 0.981 | 0.975 |
| wood | 0.930 | 0.965 |
| transistor | 0.909 | 0.918 |
| toothbrush | 1.000 | 0.972 |
| tile | 0.987 | 0.997 |
| screw | 0.845 | 0.799 |
| pill | 0.838 | 0.786 |
| metal_nut | 0.885 | 0.920 |
| leather | 1.000 | 1.000 |
| hazelnut | 0.833 | 0.890 |
| grid | 0.996 | 0.983 |
| carpet | 0.842 | 0.781 |
| capsule | 0.884 | 0.731 |
| cable | 0.819 | 0.655 |
| bottle | 0.999 | 0.971 |
2. Graphical Results
zipper
<p align="left"> <img src=assets/zipper.gif width="100%" /> </p>wood
<p align="left"> <img src=assets/wood.gif width="100%" /> </p>transistor
<p align="left"> <img src=assets/transistor.gif width="100%" /> </p>toothbrush
<p align="left"> <img src=assets/toothbrush.gif width="100%" /> </p>tile
<p align="left"> <img src=assets/tile.gif width="100%" /> </p>screw
<p align="left"> <img src=assets/screw.gif width="100%" /> </p>pill
<p align="left"> <img src=assets/pill.gif width="100%" /> </p>metal_nut
<p align="left"> <img src=assets/metal_nut.gif width="100%" /> </p>leather
<p align="left"> <img src=assets/leather.gif width="100%" /> </p>hazelnut
<p align="left"> <img src=assets/hazelnut.gif width="100%" /> </p>grid
<p align="left"> <img src=assets/grid.gif width="100%" /> </p>carpet
<p align="left"> <img src=assets/carpet.gif width="100%" /> </p>capsule
<p align="left"> <img src=assets/capsule.gif width="100%" /> </p>cable
<p align="left"> <img src=assets/cable.gif width="100%" /> </p>bottle
<p align="left"> <img src=assets/bottle.gif width="100%" /> </p> <br>3. Requirements
- CUDA 10.2
- nvidia-docker2
4. Usage
a) Download docker image and run docker container
docker pull taikiinoue45/mvtec:riad
docker run --runtime nvidia -it --workdir /app --network host taikiinoue45/mvtec:riad /usr/bin/zsh
b) Download this repository
git clone https://github.com/taikiinoue45/RIAD.git
cd /app/RIAD/riad
c) Run experiments
sh run.sh
d) Visualize experiments
mlflow ui
5. Contacts
- github: https://github.com/taikiinoue45/
- twitter: https://twitter.com/taikiinoue45/
- linkedin: https://www.linkedin.com/in/taikiinoue45/