IACV Project
Anomaly Detection Framework using Autoencoder and Denoising Network
Authors: Şemsi Yiğit Özgümüş, Yiğit Yusuf Pilavcı
Instructions
- If you don't have the data folder, in the first run model will download and create the dataset.
- All the experiment configurations and model parameters can be changed from the related config files.
- To create the same environment used in the project:
conda create --name myenv --file spec-file.txt- To run the model:
python3 train.py -c ./configs/\<CONFIGFILE\> -e \<EXPERIMENTNAME\>- You can also use the same experiment name and configuration file to continue unfinished experiment.
- Since it's a tensorflow based project, changes that affect the computation graph will result in failure to load the model. However you can modify the test_epoch() function to gain more insight about the model's predictions. To make additional predictions without training the model and by loading it :
python3 evaluate.py -c ./configs/\<CONFIGFILE\> -e \<EXPERIMENTNAME\>