Folder: anomalib_contribute
Link: https://github.com/openvinotoolkit/anomalib#1-web-based-pipeline-for-training-and-inference
THE MVTEC ANOMALY DETECTION DATASET (MVTEC AD)
MVTec AD is a dataset for benchmarking anomaly detection methods with a focus on industrial inspection. It contains over 5000 high-resolution images divided into fifteen different object and texture categories. Each category comprises a set of defect-free training images and a test set of images with various kinds of defects as well as images without defects.
Link dataset: https://www.mvtec.com/company/research/datasets/mvtec-adconda create -n anomaly-detection python=3.8
conda activate anomaly-detection
pip install -r requirements.txt
For each new dataset, the data consist of three folders:
- train, which contains the (defect-free) training images
- test, which contains the test images
- ground_truth, which contains the pixel-precise annotations of anomalous regions
python train.py --config "configs/patchcore_grid.yaml" --model "patchcore"
or download pretrained models
bash download_pretrained.sh
python script_inference.py --config "configs/patchcore_hazelnut.yaml" --weight "models/patchcore_hazelnut.ckpt" --image "samples/007_hazelnut.png"
or just simple:
python script_inference.py
python demo.py
Open local URL: http://127.0.0.1:7860
python app.py
Open local URL: http://127.0.0.1:5000
Default account login:
- Username: aicamp_batch9
- Password: 123456
docker build -t anomaly:v1 .
docker run anomaly:v1
or just simple
docker-compose up
First: Create EC2 instance
Second: git clone and install related packages
git clone https://github.com/vnk8071/anomaly-detection-in-industry-manufacturing.git
sh download_pretrained.sh
Next: install Miniconda and Docker engine
- Miniconda: Follow link https://varhowto.com/install-miniconda-ubuntu-18-04/
- Docker engine: Follow link https://docs.docker.com/engine/install/ubuntu/
docker-compose up
Final: access link http://user-IPv4-public-ec2-aws