From 712c8de9513f866653c9c97c9f89bf6b48b726b9 Mon Sep 17 00:00:00 2001 From: Qinqing Liu Date: Tue, 17 Nov 2020 14:18:51 -0500 Subject: [PATCH] Update README.md --- README.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/README.md b/README.md index 7c55ec0..b38c77e 100644 --- a/README.md +++ b/README.md @@ -47,9 +47,7 @@ cd ../../ #### Octree Generation Example Provide one example data 1A1E, also in refined-set. - Following steps can generate the points and build the OctSurf. (Default density for points is 6, and depth for OctSurf is 10.) - We also provide python tool to parse the generated OctSurf, and visualize it by generating vtk files that can visualize in Paraview. ```angular2 cd pdbbind/data_example/pdbbind @@ -68,7 +66,6 @@ cd ../ #### CNN modeling - prepare the data First it will generate the points file for each complex in general, refined, core set. (The density of points can be specificed, low resolution OctSurf can use low density points to accelerate the process, here for depth=6 model, we use density 3. Can be specified in .sh file) - Then the points and labels will be transformed into tfrecords file. ```angular2 bash data_prepare_model.sh @@ -83,7 +80,6 @@ python run_cls.py --config configs/train_resnet_depth6.yaml - test performance Specify the config files (the path for pretrained model/test dataset, network architecture, iterations etc) - Test the pre-trained model on test dataset, and report the performance. ```angular2 python test_reg_ave.py --config configs/test_resnet_depth6.yaml