Skip to content

Latest commit

 

History

History
80 lines (55 loc) · 2.68 KB

README.md

File metadata and controls

80 lines (55 loc) · 2.68 KB
Raw

ManiFest: Manifold Deformation for Few-shot Image Translation

ManiFest: Manifold Deformation for Few-shot Image Translation
Fabio Pizzati, Jean-François Lalonde, Raoul de Charette

ECCV 2022

Preview

teaser

Citation

To cite our paper, please use

@inproceedings{pizzati2021manifest,
  title={{ManiFest: Manifold Deformation for Few-shot Image Translation}},
  author={Pizzati, Fabio and Lalonde, Jean-François and de Charette, Raoul},
  booktitle={ECCV},
  year={2022}
}

Prerequisites

Please create an environment using the requirements.yml file provided.

conda env create -f requirements.yml

Download the pretrained models and the pretrained VGG used for the style alignment loss by following the link:

https://www.rocq.inria.fr/rits_files/computer-vision/manifest/manifest_checkpoints.tar.gz

Move the VGG network weights in the res folder and the checkpoints in the checkpoints one.

Inference

We provide pretrained models for the day2night, day2twilight and clear2fog tasks as described in the paper.

To perform general inference using the pretrained model, please run the following command:

python inference_general.py --input_dir <input_directory> --output_dir <output_directory> --checkpoint <checkpoint_path>

To perform exemplar inference, please use

python inference_exemplar.py --input_dir <input_directory> --output_dir <output_directory> --checkpoint <checkpoint_path> --exemplar_image <path_to_exemplar_image>

Training

We provide training code for all three tasks.

Download the ACDC, VIPER and Dark Zurich datasets.

Then, run the scripts provided in the `datasets' directory to create symbolic links.

python create_dataset.py --root_acdc <root acdc> --root_viper <root viper> --root_dz <root_dark_zurich>

To start training, modify the data/anchor_dataset.py file and choose among day2night, day2twilight or clear2fog in the root option. Finally, start the training with

python train.py --comment "review training" --model fsmunit --dataset anchor

If you don't have a WANDB api key, please run

WANDB_MODE=offline python train.py --comment "review training" --model fsmunit --dataset anchor

Code structure

When extending the code, please consider the following structure. The train.py file intializes logging utilities and set up callbacks for model saving and debug. The main training logic is in networks/fsmunit_model.py. In networks/backbones/fsmunit.py it's possible to find the architectural components.