From 6f8c78c5d8e6fc515e2d801e9ca9b0e17e7e54f6 Mon Sep 17 00:00:00 2001 From: thss15fyt Date: Mon, 17 Dec 2018 17:54:59 +0800 Subject: [PATCH] update README.md --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index a9dca33..03a2d14 100644 --- a/README.md +++ b/README.md @@ -33,10 +33,10 @@ Install [PyTorch 0.4.0](https://pytorch.org). You also need to install yaml. The Firstly, you should download the [reorganized ModelNet40 dataset](https://drive.google.com/open?id=1l8Ij9BODxcD1goePBskPkBcgKW76Ewcs). Then, configure the "data_root" in `config/train_config.yaml` and `config/test_config.yaml` with your path to the downloaded dataset: -```yaml +``` # config/train_config.yaml and config/test_config.yaml dataset: - data_root: [your_path_to_dataset] + data_root: [your_path_to_dataset] ``` For each data file `XXX.off` in ModelNet, we reorganize it to the format required by MeshNet and store it into `XXX.npz`. The reorganized file includes two parts of data: @@ -58,7 +58,7 @@ You can modify the configuration in the `config/train_config.yaml` for your own The pretrained MeshNet model weights are stored in [pretrained model](https://drive.google.com/open?id=1m5Uy9-oXMNPZ129owKvQ5ipH3f0vdABs). You can download it and configure the "load_model" in `config/test_config.yaml` with your path to the weight file. -```yaml +``` # config/test_config.yaml load_model: [your_path_to_weight_file] ```