conda env create -f environment.yml
conda activate layout

conda env update -f environment.yml --prune

export WANDB_API_KEY=<Your WANDB API KEY>

cd layout_vae

# Train the CountVAE model
python train_counts.py \
    --exp count_coco_instances \
    --train_json /path/to/coco/annotations/instances_train2017.json \
    --val_json /path/to/coco/annotations/instances_val2017.json \
    --epochs 50

# Train the BoxVAE model
python train_counts.py \
    --exp box_coco_instances \
    --train_json /path/to/coco/annotations/instances_train2017.json \
    --val_json /path/to/coco/annotations/instances_val2017.json \
    --epochs 50

# Training on MNIST layouts
python main.py \
    --data_dir /path/to/mnist \
    --threshold 1 --exp mnist_threshold_1

# Training on COCO bounding boxes or PubLayNet
python main.py \
    --train_json /path/to/annotations/train.json \
    --val_json /path/to/annotations/val.json \
    --exp publaynet

@inproceedings{gupta2021layouttransformer,
  title={LayoutTransformer: Layout Generation and Completion with Self-attention},
  author={Gupta, Kamal and Lazarow, Justin and Achille, Alessandro and Davis, Larry S and Mahadevan, Vijay and Shrivastava, Abhinav},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={1004--1014},
  year={2021}
}
}