From 615feec4d0ccc27611a3f36dc9a62e3a40434247 Mon Sep 17 00:00:00 2001 From: Yilai Li <44369625+yilaili@users.noreply.github.com> Date: Sat, 7 Mar 2020 10:28:38 -0500 Subject: [PATCH] update readme file --- README.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/README.md b/README.md index 265709f..a1f0c7f 100644 --- a/README.md +++ b/README.md @@ -39,7 +39,9 @@ You will need the pre-trained model files to run MicAssess and 2DAssess. To down **MicAssess:** Note: MicAssess currently works on micrographs from both K2 and K3 camera. + Note: MicAssess currently does not support star file from Relion 3.1. + You will need to activate the conda environment by ```conda activate cryoassess``` before using MicAssess. To run MicAssess: @@ -48,11 +50,17 @@ micassess -i -m ``` Optional arguments: + -d, --detector: Either "K2" or "K3". Default is "K2". + -o, --output: Name of the output star file. Default is good_micrographs.star. + -b, --batch_size: Batch size used in prediction. Default is 32. Increasing this number will result in faster prediction, if your GPU memory allows. If memory error/warning appears, you should lower this number. + -t, --threshold: Threshold for classification. Default is 0.1. Higher number will cause more good micrographs being classified as bad. + --threads: Number of threads for conversion. Default is None, using mp.cpu_count(). If get memory error, set it to a reasonable number (e.g. 10). This usually happens when you have super-resolution microgarphs from K3. + --gpus: Specify which GPU(s) to use, e.g. 0,1,2,3. Default is 0, which uses only the first GPU. The input of MicAssess could be a .star file with a header similar to this: