Deformable Encoder Transformer (DEnT)

Switch to branch three

git switch three

Dataset

Download and extract the pickle dataset from link

wget -O pickle.zip <link>
unzip pickle.zip

Generate positive and negative pairs

cd dent/
python util/util.py

The directory structure should be similar to

- scr
  - dent
    - data
      - positive.txt
      - negative.txt
      - ...
  - pickle
  - yolov5
  - fasterrcnn

Train the pre-training model

cd dent/
# to run with default arguments
python pretrain.py

# to modify arguments
python pretrain.py --root <str> --world_size <int> --resume <bool> --resume_weight <str> --train_folder <str> --val_folder <str> --epochs <int> --folds <int> --cf <int> --batch_size <int>

Train the detection model

cd dent/

# to run with default arguments
python train.py

# to modify arguments
python train.py --root <str> --dataroot <str> --world_size <int> --resume <bool> --resume_weight <str> --pretrain <bool> --pretrain_weights <str> --epochs <int> --nc <int> --r <int> --space <int> --train_batch <int> --val_batch <int>

Run validation directly on the detection model

cd dent/

# to run with default arguments
python validate.py

# to modify arguments
python validate.py --root <str> --dataroot <str> --world_size <int> --weights <str> --nc <int> --r <int> --space <int> --batch <int>