Awesome
Metrics of GANs
- Fréchet Inception Distance (FID)
- Inception Score (IS)
- Number of statistically-Different Bins (NDB)
- Jensen-Shannon Divergence (JSD)
- Learned Perceptual Image Patch Similarity (LPIPS)
| Metric | Usage | Notes |
|---|---|---|
| FID | Image Quality | the lower, the better |
| IS | Image Quality | the higher, the better |
| NDB | Diversity | the lower, the better |
| JSD | Diversity | the lower, the better |
| LPIPS | Diversity | the higher, the better |
Configuration
See the environment.yaml. We provide an user-friendly configuring method via Conda system, and you can create a new Conda environment using the command:
conda env create -f environment.yaml
Usage
- IS:
python eval.py --metric is --pred_list <path/to/pred_list> --gpu_id 0 --resize 299
- FID:
python eval.py --metric fid --pred_list <path/to/pred_list> --gt_list <path/to/gt_list> --gpu_id 0 --resize 299
- NBD & JSD:
python eval.py --metric ndb --pred_list <path/to/pred_list> --gt_list <path/to/gt_list> --gpu_id 0 --resize 128
- LPIPS:
python lpips.py --path <path/to/image_folder> --test_list <path/to/test_list>
Example of test list in LPIPS:
a1.png a2.png
a1.png a3.png
...