Anomaly-Segmentation-in-a-teacher-guide-student-manner

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Anomaly-Segmentation-in-a-teacher-guide-student-manner

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# Anomaly Segmentation in a teacher-guide-student manner

This is a toy example to do anomaly segmentation using a quite simple teacher-student framework. I wrote this project for learning deeplearning and pytorch. My code is heavily borrowed from this **SPADE-pytorch** project. https://github.com/byungjae89/SPADE-pytorch

![avatar](https://raw.githubusercontent.com/plutoyuxie/Anomaly-Segmentation-in-a-teacher-guide-student-manner/master/bottle_000.png)

The main difference is that here a pretrained vgg16 is used as a teacher, guiding a student network with the same structure to extract features. **The student's regression errors with respect to the teacher in different receptive fields** are combined to yield dense anomaly scores.

## Requirement
`PyTorch 1.5+`

## Datasets
MVTec AD datasets https://www.mvtec.com/company/research/datasets/mvtec-ad/

## Code examples

### Step 1. Set the *DATASET_PATH* variable.

Set the [DATASET_PATH](main.py#L022) to the root path of the downloaded MVTec AD dataset.

### Step 2. Train vgg16 student and Test.

`python main.py`

## Discussion
- **evaluation metric** 
**roc_auc_score** is not suitable because of the extreme imbalance between possitive pixels and negative pixels. 
**per-region-overlap(PRO)** should be a better evaluation metric in this scenario.

Anomaly-Segmentation-in-a-teacher-guide-student-manner

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