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Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 14 |
1 files changed, 7 insertions, 7 deletions
@@ -1,5 +1,5 @@ # Future Frame Prediction for Anomaly Detection -- A New Baseline -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. +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, Weixin Lluo, 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) @@ -8,7 +8,7 @@ A **demo** is shown in *https://www.youtube.com/watch?v=M--wv-Y_h0A*. It is impl numpy==1.14.1 scipy==1.0.0 matplotlib==2.1.2 -tensorflow_gpu==1.4.1 +tensorflow-gpu==1.4.1 tensorflow==1.4.1 Pillow==5.0.0 pypng==0.0.18 @@ -37,7 +37,7 @@ cd Data ## 3. Testing on saved models * Download the trained models ```shell -cd models +cd checkpoints ./download_pretrains.sh ``` * Running the sript (as ped2 and avenue datasets for examples) and cd into **Codes** folder at first. @@ -45,14 +45,14 @@ cd models python inference.py --dataset ped2 \ --test_folder ../Data/ped2/testing/frames \ --gpu 1 \ - --snapshot_dir models/pretrains/ped2 + --snapshot_dir checkpoints/pretrains/ped2 ``` ```shell python inference.py --dataset avenue \ --test_folder ../Data/avenue/testing/frames \ --gpu 1 \ - --snapshot_dir models/pretrains/avenue + --snapshot_dir checkpoints/pretrains/avenue ``` @@ -111,8 +111,8 @@ Open the browser and type **https://ip:10086**. Following is the screen shot of   -Since all frames are loaded into BGR channels in training and testing, the visualized images look different from RGB channels. - +Since the models are trained in BGR image color channels, the visualized images in tensorboard look different from RGB channels. +In the demo, we change the output images from BGR to RGB. ## Notes The flow loss (temporal loss) module is based on [a TensorFlow implementation of FlowNet2](https://github.com/sampepose/flownet2-tf). Thanks for their nice work. |