From c2a85686657a033277bd595abdc2739ce371a670 Mon Sep 17 00:00:00 2001 From: Shean Date: Mon, 18 Sep 2017 00:18:29 +0800 Subject: [PATCH] Minor fixes in README --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 9b1c87e..209f5ec 100644 --- a/README.md +++ b/README.md @@ -15,11 +15,11 @@ Prerequisites: You can use the `Dockerfile` provided to build the environment then enter the environment using `nvidia-docker run --rm -it -v HOST_FOLDER:/share DOCKER_IMAGE bash`. To train the model, just run `python start_train.py`. Default configuration can be found at `config.yml`. You need to prepare video dataset you plan to train/evaluate on. You may get the benchmark dataset videos from respective authors. For each dataset, put the training videos into `VIDEO_ROOT_PATH/DATASET_NAME/training_videos` and testing videos into `VIDEO_ROOT_PATH/DATASET_NAME/testing_videos`. Example structure of training videos for `avenue` dataset: -`VIDEO_ROOT_PATH/avenue/training_videos` -- `01.avi` -- `02.avi` -- ... -- `16.avi` +- `VIDEO_ROOT_PATH/avenue/training_videos` + - `01.avi` + - `02.avi` + - ... + - `16.avi` Once you have trained the model, you may now run `python start_test.py` after setting the parameters at the beginning of the file.