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ProTeCt [project page]

This repository contains the source code accompanying our CVPR 2024 paper.

ProTeCt: Prompt Tuning for Taxonomic Open Set Classification
Tz-Ying Wu, Chih-Hui Ho, Nuno Vasconcelos

Dependencies

  • Python (3.8.16)
  • PyTorch (1.12.1)
  • torchvision (0.13.1)
  • NumPy (1.23.5)
  • Pillow (9.4.0)
  • PyYaml (6.0)
  • tensorboardX (2.6)

We also provide the pre-built conda environment for easier setup. Simply run

$ conda env create -f environment.yml
$ conda activate ProTeCt

Data preparation

These datasets can be downloaded from the above links. Please put the raw images in the folder prepro/raw/. We have preprocessed the data lists (gt_{split}.txt), hierarchy (tree.npy), and treecuts for evaluating MTA (treecuts_*.pkl) in prepro/data/ using the splits in prepro/splits/. Only the former is required for running the code, and the latter is just for reference. Note that prepro/prepro.py can be used if you want to build the data lists and the tree hierarchies of other datasets.

Training

To train the model with different datasets and model configurations (e.g. CoOp/MaPLe/CoOp+ProTeCt/MaPLe+ProTeCt), you need to indicate the config file. We provide the config templates in the folder configs. For example,

$ python train.py --config configs/{dataset}/few_shot/16_shot/CoOp+ProTeCt.yml --trial 1

where {dataset}=cifar100/sun/imagenet. Note that training will automatically create log files (including txt and tfevents) and model checkpoints, and the default folder is under runs/.

Evaluation

To evaluate a model, you need to indicate the folder of the experiment. For example,

$ python test.py --folder runs/{dataset}/coop/ViT-B_16/few_shot/16-shot/CoOp/trial_1 --bz {batch_size}

where {dataset}=cifar100/sun/imagenet, and you can set the batch size for testing with {batch_size} based on your GPU memory.

For testing across datasets for the imagenet transfer experiments, you need to set the {eval_dataset}

$ python test.py --folder runs/{dataset}/coop/ViT-B_16/few_shot/16-shot/CoOp/trial_1 --bz {batch_size} --eval_dataset {eval_dataset}

where {eval_dataset}=imagenet-a/imagenet-r/imagenetV2/imagenet-sketch.

Cite

If you find this repository useful, please consider cite our paper.

@InProceedings{Wu_2024_CVPR,
author = {Wu, Tz-Ying and Ho, Chih-Hui and Vasconcelos, Nuno},
title = {ProTeCt: Prompt Tuning for Taxonomic Open Set Classification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2024}
}