IDAO-21 Baseline

Dataset

Dataset is available for download through Yandex disk -> Click here.

Each directory contain images in .png format, the filename contain the class label information.

_dataset/
├── private_test
│   └── *.png
├── publis_test
│   └── *.png
└── train
    ├── ER
    └── NR

Each directory contain bunch of images the image name contain the class label and other informaion. The most important part is the class label which is in {1.0, 3.0, 6.0, 10.0, 20.0, 30.0} and has _keV suffix.

0.00012752991074573__CYGNO_60_40_ER_30_keV_930V_30cm_IDAO_iso_crop_hist_pic_run4_ev846;1.png
0.0001805024851534__CYGNO_60_40_ER_10_keV_930V_30cm_IDAO_iso_crop_hist_pic_run2_ev317;1.png
0.00013557143164375__CYGNO_60_40_ER_10_keV_930V_30cm_IDAO_iso_crop_hist_pic_run2_ev842;1.png
0.00019057084775703__CYGNO_60_40_ER_3_keV_930V_30cm_IDAO_iso_crop_hist_pic_run2_ev116;1.png
0.0001135022106767__CYGNO_60_40_ER_10_keV_930V_30cm_IDAO_iso_crop_hist_pic_run5_ev136;1.png
0.0001275016178883__CYGNO_60_40_ER_3_keV_930V_30cm_IDAO_iso_crop_hist_pic_run2_ev485;1.png
0.0001375808674508__CYGNO_60_40_ER_30_keV_930V_30cm_IDAO_iso_crop_hist_pic_run3_ev662;1.png
0.0011665058173393__CYGNO_60_40_ER_10_keV_930V_30cm_IDAO_iso_crop_hist_pic_run5_ev574;1.png
0.0011465791675372__CYGNO_60_40_ER_3_keV_930V_30cm_IDAO_iso_crop_hist_pic_run2_ev114;1.png
0.0011065850424555__CYGNO_60_40_ER_3_keV_930V_30cm_IDAO_iso_crop_hist_pic_run4_ev868;1.png

Data spliting

The total number of samples in the dataset is ~30k sample distributed with 2 classes ER=0 and NR=1. The dataset are interleaved in the following scheme:

Interleave the files

Energy	He	e
1	*	-
3	-	*
6	*	-
10	-	*
20	*	-
30	-	*

• is training; - is testing

Dataset distribution

Training

If you want to retrain the model just run:

mv checkpoints checkpoints_bk && python train.py

This will move the original checkpoints and run the experiment again. Note you can modify config.ini to loader the checkpoint without moving the files.

Results

To generate the report just run:

python report.py

This will generate ./resultsreport.log in the current directory containg information and bunch of plots in the ./results/ directory

Score

Score = MAE - AUC

Classification

Regression

Submission

To generate submission.csv run:

python generate_submission.py