Image Fusion Transformer

Related tags

Deep Learning Image-Fusion-Transformer
Overview

Image-Fusion-Transformer

Platform

Python 3.7
Pytorch >=1.0

Training Dataset

MS-COCO 2014 (T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, and C. L. Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 3-5.) is utilized to train our auto-encoder network.

KAIST (S. Hwang, J. Park, N. Kim, Y. Choi, I. So Kweon, Multispectral pedestrian detection: Benchmark dataset and baseline, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1037–1045.) is utilized to train the RFN modules.

The testing datasets are included in "analysis_MatLab".

Training Command:

python train_fusionnet_axial.py

Testing Command:

python test_21pairs_axial.py

The Fusion results are included in "analysis_MatLab".

If you have any questions about the code, feel free to contact me at [email protected].

Acknowledgement

This codebase is built on top of RFN-Nest by Li Hui.

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Comments
  • KAIST dataset

    KAIST dataset

    Many thanks for your work. I go to their project homepage but it seems the dataset is unavailable So I am sorry to ask how to download KAIST dataset?

    opened by yarqian 3
  • `with torch.no_grad()` in your `train_fusionnet_axial.py` ?

    `with torch.no_grad()` in your `train_fusionnet_axial.py` ?

    In your training script, you use with torch.no_grad() in line 56 to build your nest_model.

    It means only fusion_model are trained during the training process?

    If I want to train on my own dataset, should I remove with torch.no_grad() ?

    opened by shinianzhihou 1
Owner
Vibashan VS
PhD at Johns Hopkins University
Vibashan VS
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