From bfee5daff018b2555c0f82270bf0468917e7c5c5 Mon Sep 17 00:00:00 2001 From: StevenLiuWen Date: Tue, 13 Mar 2018 03:46:45 -0400 Subject: Update README.md --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 7985c76..0441115 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ This repo is the official open source of [Future Frame Prediction for Anomaly Detection -- A New Baseline, CVPR 2018](https://arxiv.org/pdf/1712.09867.pdf) by Wen Liu, Weixinluo, Dongze Lian and Shenghua Gao. A **demo** is shown in *https://www.youtube.com/watch?v=M--wv-Y_h0A*. It is implemented in tensorflow. Please follow the instructions to run the code. -####1. Installation (Anaconda with python3.6 installation is recommended) +#### 1. Installation (Anaconda with python3.6 installation is recommended) * Install 3rd-package dependencies of python (listed in requirements.txt) ``` numpy==1.14.1 @@ -24,7 +24,7 @@ pip install -r requirements.txt CUDA 8.0 Cudnn 6.0 ``` -####2. Download datasets +#### 2. Download datasets cd into Data folder of project and run the shell scripts (**ped1.sh, ped2.sh, avenue.sh, shanghaitech.sh**) under the Data folder. ```shell cd Data @@ -34,7 +34,7 @@ cd Data ./shanghaitech.sh ``` -####3. Testing on saved models +#### 3. Testing on saved models * Download the trained models ```shell cd models @@ -56,7 +56,7 @@ python inference.py --dataset avenue \ ``` -####4. Training from scratch (here we use ped2 and avenue datasets for examples) +#### 4. Training from scratch (here we use ped2 and avenue datasets for examples) * Set hyper-parameters The default hyper-parameters, such as $\lambda_{init}$, $\lambda_{gd}$, $\lambda_{op}$, $\lambda_{adv}$ and the learning rate of G, as well as D, are all initialized in **training_hyper_params/hyper_params.ini**. * Running script (as ped2 or avenue for instances) and cd into **Codes** folder at first. -- cgit v1.2.3-70-g09d2