The following packages were used to evaluate the model.
- python==3.8.8
- pytorch==1.7.1
- torchvision==0.8.2
- cudatoolkit==10.1.243
- opencv-python==4.5.1.48
- numpy==1.19.2
- pillow==8.1.2
- cupy==9.0.0
Installation with anaconda:
conda create -n edc python=3.8.8
conda activate edc
conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.1 -c pytorch
conda install -c conda-forge cupy
pip install opencv-python==4.5.1.48
- Download UCF101 quintuplets from here.
- Download DAVIS sequences from here.
- Download VFITex dataset from here.
The dataset folder names should be lower-case and structured as follows.
└──── <data directory>/
├──── ucf101/
| ├──── 0/
| ├──── 1/
| ├──── ...
| └──── 99/
├──── davis90/
| ├──── bear/
| ├──── bike-packing/
| ├──── ...
| └──── walking/
├──── snufilm/
| ├──── test-easy/
| ├──── test-medium/
| ├──── test-hard/
| ├──── test-extreme/
| └──── data/
└──── vfitex/
├──── beach02_4K_mitch/
├──── bluewater_4K_pexels/
├──── ...
└──── waterfall_4K_pexels/
Download the pre-trained network from here.
python evaluate.py \
--net EDC \
--data_dir <data directory> \
--checkpoint <path to pre-trained model (.pth file)> \
--out_dir eval_results \
--dataset <dataset name>
where <dataset name> should be the same as the class names defined in data/testsets.py, e.g. Snufilm_extreme_quintuplet.
@misc{danier2022enhancing,
title={Enhancing Deformable Convolution based Video Frame Interpolation with Coarse-to-fine 3D CNN},
author={Duolikun Danier and Fan Zhang and David Bull},
year={2022},
eprint={2202.07731},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Lots of code in this repository are adapted/taken from the following repositories:
We would like to thank the authors for sharing their code.