This repository contains the code for the paper Robust Autofocus Score Prediction of SEM on Magnification Based on Deep Learning System presented at Microscopy & MicroAnalysis (M&M 2020).
#gpu CUDA 10.0 cudnn 7.6.0 #package pytorch 1.1.0 torchvision 0.3.0 numpy 1.16.4 matplotlib 3.1.1 pillow 6.1.1 - Datasets are prohibited to share.
- Datasets should be like this if you have datasets and want to run training code.
mnm2019_dataset/ ├── 01220_grid+tinball ├── grid_tinball_zero_20200206_refined ├── Data_0 └── cache.txt mnm2019_dataset/ ├── train/ ├── test/ └── cache.txt #head of cache.txt 24501 2734 3037 # number of train, valid, test Data_0/169.jpg 0 500 0 # path_of_image, experts_score, magnification, specimen Data_0/362.jpg 0 500 0 Data_0/420.jpg 0 2000 0 Data_0/402.jpg 0 1000 0 ... python train.py <opt> # opt: a, b, c, d - a: proposed model with new dataset
- b: proposed model with old dataset
- c: resnet50 with new dataset
- d: resnet50 with old dataset
Please download below models and make folder pretrained/ and place models in that folder by unzipping mnm2020_models.zip.
- models : Google Drive
pretrained ├── mnm2020_models.zip ├── mnm2020_with_new.pth ├── mnm2020_with_old.pth ├── mnm2019_with_new.pth └── mnm2019_with_old.pth python test.py <opt> # opt: a, b, c, d - a: proposed model with new dataset
- b: proposed model with old dataset
- c: resnet50 with new dataset
- d: resnet50 with old dataset
If you want to test log,
python test.py <opt> > <name>.txt There are 6 algorithms of sharpness functions we implemented. You can find those in af/sharpness.py. To compare performance, we only use variance absolute which is the best performing conventional algorithm in previous study.
cd af/ python autofocus.py <dataset path> <sharpness function> #Example: python autofocus.py ../../data/dataset/ 
You can see our saved results in results/.
#(option) filename.txt (a) mnm2020_with_new.txt (b) mnm2020_with_old.txt (c) mnm2019_with_new.txt (d) mnm2019_with_old.txt (var_abs) var_abs.txt @proceeding{deep-autofocus, title = {Robust Autofocus Score Prediction of SEM on Magnification Based on Deep Learning System}, author = {Moohyun Oh, Jonggyu Jang, Hyun Jong Yang, Hyeonsu Lyu}, journal = {arXiv or google scalar, **NEED TO UPDATE LATER} year = {2020}, howpublished = {\url{https://github.com/blacknwhite5/deep-autofocus}} }