cryolo_reference_example

This version (2019/09/23 09:30) was approved by twagner.The Previously approved version (2019/09/23 09:27) is available.Diff

crYOLO reference example

Here we provide quick run through example for training and picking with crYOLO. The main purpose is to check if your setup is running as expected. I will not provide detailed explanations in this text. Please note that there is a detailed tutorial.

We run this example on a machine with the following specification:

  • Titan V
  • Intel Core i9 7920X @ 2.90 Ghz
  • SSD Harddrive
  • crYOLO 1.5.0

You can download the reference data (TcdA1) here:

Link to reference data

Then unzip the data:

unzip toxin_reference.zip -d toxin_reference/
cd toxin_reference

The toxin_reference directory contains multiple folders / files:

  • train_image: Folder with 12 training images
  • train_annot: Folder with 12 box files for the training images
  • config_phosnet.json: Configuration file for crYOLO
  • reference_model.h5: Model that I've trained on my machine using the commands below.
  • reference_results: Picked particles using my machine and the reference model.

Before you start training / picking please activate your environment:

source activate cryolo

The training is done with this command:

cryolo_train.py -c config_phosnet.json -w 5 -e 5 -g 0

It needes 5 minutes 50 seconds to converge (5 warmup + 10 “normal” epochs). The best validation loss was 0.03042. These numbers might be a little bit different on your case.

cryolo_predict.py -c config_phosnet.json -w model.h5 -i unseen_examples/ -o my_results

It picked 1617 particles on 12 micrographs in 3 seconds. Including filtering the image and loading the model the command needed 38 seconds.

cryolo_boxmanager.py -i unseen_examples/ -b my_results/CBOX/
  • cryolo_reference_example.txt
  • Last modified: 2019/09/23 09:28
  • by twagner