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.
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:
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 imagestrain_annot
: Folder with 12 box files for the training imagesconfig_phosnet.json
: Configuration file for crYOLOreference_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:
>>> 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¶
>>> 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¶
>>> cryolo_boxmanager.py -i unseen_examples/ -b my_results/CBOX/