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Spectral Transfer Guided Active Domain Adaptation for Thermal Imagery

If you make use of this code, please cite the following paper (and give us a star ✨):

@InProceedings{stgada2023,
    author    = {Ustun, Berkcan and Kaya, Ahmet Kagan and Cakir Ayerden, Ezgi and Altinel, Fazil },
    title     = {Spectral Transfer Guided Active Domain Adaptation for Thermal Imagery},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2023},
    pages     = {4322-4331}
}

arXiv

Overview

This repository contains official implementation of "Spectral Transfer Guided Active Domain Adaptation for Thermal Imagery" paper (accepted to CVPR 2021 Perception Beyond the Visible Spectrum (PBVS) workshop).

Environment

  • Python 3.7
  • Pytorch 1.8.0
  • torchvision 0.9
  • Numpy 1.20

Dataset Preparation

  • Download FLIR ADAS dataset: Link
  • Download MS-COCO dataset:
  • After you downloaded the datasets, prepare the dataset .txt files similar to the files in 'RGB' folder and put them in that folder.

Running

  1. Modify the 'ini.config' file.
  2. To train STGADA, run the command below.
(stgada) $ python run.py
Parameter Name Type Definition
[path] str Path to the txt files
[source] str Name of the Source dataset
[target] str Name of the Target dataset
[target_test] str Name of the Target Test dataset
[lr] float Learning rate
[batch] int Batch size
[stgada_lambda] float Lambda parameter
[stgada_margin] float Margin parameter
[l] float l parameter of FDA (the beta parameter)
[device] int GPU device ID
  • Log

We also provide our experiment logs saved in RGB_result/{dataset_source}_{dataset_target}.log. For example, mscoco_flir.log and the best model along with the models saved after the active epochs.

Acknowledgement

This repo is mostly based on: