Feature distribution alignments for object detection in the thermal domain

📖 Feature distribution alignments for object detection in the thermal domain

[Paper]   [Project Page]   [Demo]
Mohamed Amine Marnissi ...

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🔧 Dependencies and Installation

• Python >= 3.7 (Recommend to use Anaconda or Miniconda)
• PyTorch >= 1.7
• Option: NVIDIA GPU + CUDA
• Option: Linux

Installation

We now provide a clean version of GFPGAN, which does not require customized CUDA extensions.

1. Clone repo

   git clone https://github.com/AmineMarnissi/UDAT.git
   cd UDAT

2. Install dependent packages

   # Create the environment from the environment.yml file:
   conda env create -f environment.yml
   
   # Activate the new environment:
   conda activate UDAT
   
   # Verify that the new environment was installed correctly:
   conda env list

Datasets

• KAIST: Download the Thermal KAIST and Visible KAIST dataset.
• FLIR: Download the Thermal FLIR and Visible FLIR dataset.

Models

Pre-trained Models

In our experiments, we used two pre-trained models on ImageNet, i.e. ResNet50. Please download the model from:

• ResNet50: Dropbox, VT Server

Download and make the model in data/pretrained_model/.

Training

bash kaist_train.sh

bash flir_train.sh

Test

python test_flir.py

Demo

BibTeX

@article{article,
author = {Marnissi, Mohamed and Fradi, Hajer and Sahbani, Anis and ESSOUKRI BEN AMARA, Najoua},
year = {2022},
month = {02},
title = {Feature distribution alignments for object detection in the thermal domain},
journal = {The Visual Computer}
}

📧 Contact

If you have any question, please email mohamed.amine.marnissi@gmail.com.