cryolo_reference_example

This version (2019/07/10 21:02) was approved by twagner.

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

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

First, you have to warm up the network with “-g 0” I'm selection GPU 0. It takes 2 minutes 30 seconds on our reference setup:

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

The actual training is done with this command:

cryolo_train.py -c config_phosnet.json -w 0 -e 10 -g 0

It needed 6 minutes 45 seconds to converge (18 epochs). The best validation loss was 0.03316. 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 4 seconds. Including filtering the image and loading the model the command needed 46 seconds.

cryolo_boxmanager.py -i unseen_examples/ -b my_results/CBOX/
  • cryolo_reference_example.txt
  • Last modified: 2019/04/30 18:30
  • by twagner