This is a third party implementation of RA-CNN in pytorch. I am still working on reproducing a same performance written in paper
- python3
- Pytorch 0.4 >
- torchvision
- numpy
- tensorflow, optional
- Network building
- Repactoring for arguments
- Pre-training a APN
- Alternative training between APN and ConvNet/Classifier
- Reproduce and report on README.md
- Sample visualization
- Add new approach to improve
- Don't know how to pre-train a APN. Need more details
- Rankloss doesn't decrease. Because no pretrain? or bugs?
Current best is 71.68% at scale1 without APN pretraining. It's bad than using just VGG19
For training, use following command.
$ python trainer.pyCurrently only cuda available device support.
If you want to see training process,
$ Tensorboard --log='visual/' --port=6666and go to 'localhost:6666' on webbrowser. You can see the Loss, Acc and so on.
- Original code
- Other pytorch implementation
- with car dataset, I refer the attention crop code from here