FCViT

Solving Jigsaw Puzzles by Predicting Fragment’s Coordinate Based on Vision Transformer

Garam Kim^a, Hyeonseong Cho^a *, Hyoungsik Nam^a *

^a Kyung Hee University, Republic of Korea

(*) Corresponding Authors

ESWA 2025

FCViT: Fragment’s Coordinate prediction Vision Transformer

• This repository contains PyTorch training code and evaluation code for FCViT.
• Architecture of FCViT:
• 
• For details see Solving Jigsaw Puzzles by Predicting Fragment’s Coordinate Based on Vision Transformer by Garam Kim, Hyeonseong Cho and Hyoungsik Nam.
• If you use this code for a paper please cite:

• @article{kim2025solving,
  title={Solving jigsaw puzzles by predicting fragment’s coordinate based on vision transformer},
  author={Kim, Garam and Cho, Hyeonseong and Nam, Hyoungsik},
  journal={Expert Systems with Applications},
  volume={272},
  pages={126776},
  year={2025},
  publisher={Elsevier}
  }
  

Catalog

• Overview of paper
• Usage
• Data preparation
• Evaluation code
• Training code
• License

Overview of paper

• Overview in English
• Overview in Korean

Usage

• First, clone the repository locally:

• git clone https://github.com/HiMyNameIsDavidKim/fcvit.git
  

• Then, install PyTorch and torchvision and timm==0.4.12:

• conda install -c pytorch pytorch torchvision
  pip install timm==0.4.12
  

Data preparation

• Download ImageNet train and val images from http://image-net.org/.
• The directory structure is the standard layout for the torchvision datasets.ImageFolder, and the training and validation data is expected to be in the train/ folder and val/ folder respectively:

• /path/to/imagenet/
    train/
      class1/
        img1.jpeg
      class2/
        img2.jpeg
    val/
      class1/
        img3.jpeg
      class2/
        img4.jpeg
  

Evaluation code

• To evaluate a FCViT-base on ImageNet val with a GPU:

• python main_eval.py \
  --eval \
  --backbone vit_base_patch16_224 \
  --size_puzzle 225 \
  --size_fragment 75 \
  --num_fragment 9 \
  --batch_size 64 \
  --resume FCViT_base_3x3_ep100_lr3e-05_b64.pt \
  --data_path ${IMAGENET_DIR}
  

Training code

• To train FCViT-base on ImageNet on a GPU for 100 epochs run:

• python main_train.py \
  --backbone vit_base_patch16_224 \
  --size_puzzle 225 \
  --size_fragment 75 \
  --num_fragment 9 \
  --lr 3e-05 \
  --epochs 100 \
  --weight_decay 0.05 \
  --batch_size 64 \
  --data_path ${IMAGENET_DIR} \
  --output_dir ${SAVE_DIR}
  

Acknowledgments

• Our codebase is mainly based on JigsawCFN, MAE, ViT and timm.
• This work was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2019R1F1A1061114) and the Brain Korea 21 Four Program in 2022.