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Brown_data DigNet May 1, 2018
RAL_net_result_models
Result
Evaluation_L2.py DigNet May 1, 2018
Get_data.py RAL-Net Nov 1, 2018
Losses.py
RAL_Net.py RAL-Net Nov 1, 2018
RAL_loss.py
RAL_net_cov.py
README.md Update README.md Mar 6, 2019
Utils.py

README.md

RAL-Net

This code is the implementation of our ACCV2018 paper Robust Angular Local Descriptor Learning

Enviromental built:

Please install python 3.6, pytorch 0.4.1, opencv 3.4

pip install python==3.6

conda install pytorch=0.4.1 cuda90 -c pytorch

To replicate the result of this paper:

python RAL_Net.py --data-root='your data root' --epochs=10 --batch-size=512 --n-pairs=5000000 --loss-type=RAL_loss --lr=10 --augmentation=True

Result:

Performance comparison on Brown dataset, lower score and perform better

Performance comparison on Hpatches dataset, Higher score and perform better

Performance comparison on Wxbs dataset, Higher score and perform better

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