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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 <tutorial-label>.

Reference setup

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

Download reference data and getting started

You can download the reference data (TcdA1) here:

Link to reference example

Then unzip the data:

bash $

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

Training

The training is done with this command:

bash $

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

crYOLO needs 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.

Prediction

bash $

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.

Visualize results

bash $

napari_boxmanager 'unseen_examples/.mrc' 'my_results/CBOX/.cbox'