N Dinesh Reddy, Robert Tamburo, Srinivasa G. Narasimhan
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[Project] [Paper] [Supp][Dataset] [Bibtex ]
All the stable releases of docker-ce installed from https://docs.docker.com/install/
Setting up the docker
docker build -t walt docker/We Will show the steps to follow to train the walt network to produce amodal segmentation results on any camera in the wild.
Firstly you need to generate the CWALT data composition. To do that we need to download the walt dataset from HERE.
The final folder format to train on carfusion data needs to look :
WALT
└─data
└─cam2
└─cam2.json
└─week1
2021-05-01T00-02-23.775683.jpg
2021-05-01T00-44-55.207427.jpg
...
└─week2
2021-05-08T00-00-59.416878.jpg
2021-05-08T00-18-03.210882.jpg
...
...
└─T01-median_image.jpg
T02-median_image.jpg
T03-median_image.jpg
...
Then CWALT dataset can be generated using
docker run --gpus all --shm-size=8g -v $PWD:/code walt python cwalt_generate.py
For Training the WALNET for the data use the following script:
sudo docker run --gpus all --shm-size=24g -v $PWD:/code walt python train.py configs/walt/walt_vehicle.pyFor Evaluations of the trained model run the following commands
sudo docker run --gpus all --shm-size=24g -v $PWD:/code walt python test.py configs/walt/walt_vehicle.py {Trained Model}.pth --eval bboxInference on the images with occlusions run
mkdir data/models
wget https://www.cs.cmu.edu/~walt/models/walt_people.pth -O data/models/walt_people.pth
wget https://www.cs.cmu.edu/~walt/models/walt_vehicle.pth -O data/models/walt_vehicle.pth
sudo docker run --gpus all --shm-size=24g -v $PWD:/code walt python infer.py Using Gradio we created a very simple cpu dependent demo of our network which can be run on any computer.
virtualenv waltdemo -p python3
source waltdemo/bin/activate
pip install -r requirements.txt
python3 app.pyWe have a live demo online at [CVPR WALT DEMO] and [WALT DEMO]
@InProceedings{Reddy_2022_CVPR,
author = {Reddy, N. Dinesh and Tamburo, Robert and Narasimhan, Srinivasa G.},
title = {WALT: Watch and Learn 2D Amodal Representation From Time-Lapse Imagery},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {9356-9366}
}
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