VIT Tuner

Powerful and efficient VIT tuning tool: Integrating Feature Distillation, Masked Autoencoder, and FT-CLIP techniques.

Installation

1. Use python version 3.10.0

   pyenv local 3.10.0

2. Create virtualenv & activate

   python -m venv vit-tuner-env
   source vit-tuner-env/bin/activate

3. Install dependencies

   pip install -r requirements.txt

Command

1. Train Model

   python tools/train.py --config_file ${config_file} \
                         --exp_name ${exp_name} \
                         --csv_path ${csv_path} \
                         --image_dir ${image_dir} \
                         --split_col ${split_col} \
                         --n_epochs ${n_epochs} \
                         --y_col ${y_col} \
                         --bs ${bs} \
                         --lr ${lr} \
                         --optim ${optim} \
                         --weight_decay ${weight_decay} \
                         --weight ${weight} \
                         --device ${device} \
                         --num_workers ${num_workers} \
                         [--debug]

   • config_file: Configuration file path. All configuration files are in configs/ directory.
   • exp_name: Tag of the experiment. All experimental outputs, including model weights, training records will save at checkpoints/{exp_name} folder.
   • csv_path: csv file that contains ground truth information. The file should contain ground truth column(s) and the column for spliting training and testing set.
   • image_dir: Path to image directory.
   • n_epochs: Number of epochs.
   • y_col: Column name(s) corresponding to the ground truth(s).
   • bs: Batch size.
   • lr: Learning rate.
   • optim: Type of optimizer. SHould be one of "Adam", "SGD", "AdamW".
   • weight_decay: Weight decay for regularization.
   • weight: MOdel weight path.
   • device
   • num_workers

TODO

1. Model Inference
2. Linear Probe
3. Model EMA