There are 2 requirements.txt, one for Ubuntu and another for Windows.
Use as follows: pip intall -r requirements.txt
In the save/ directory, I have pretrained weights (se_weights_epoch_0.pth) that achieve :
- Source accuracy: 98 %
- Target accuracy: 97 %
- F1 target: 1.000
- Precision target: 1.000
- Recall target: 1.000
usage: domain_adaptation.py [-h] [--resume RESUME] [--data DATA] [--save SAVE]
[--eval EVAL] [--batch_s BATCH_S]
[--batch_t BATCH_T] [--disp]
SVHN to MNIST domain adaptation
optional arguments:
-h, --help show this help message and exit
--resume RESUME Weights path to resume from
--data DATA Datasets path
--save SAVE Result path
--eval EVAL Evaluate model
--batch_s BATCH_S Source domain batch size
--batch_t BATCH_T Target domain batch size
--disp Display predictions during evalpython domain_adaptation.py --resume=./save/se_weights_epoch_0.pth --eval 100 --batch_s 10 --batch_t 10python domain_adaptation.py --resume=./save/se_weights_epoch_0.pth --eval 1 --disp --batch_s 10 --batch_t 10Close the matplotlib windows (alt-f4 or close button) to get new batch prediction. Ctrl-C to stop.
python domain_adaptation.py --resume=./save/se_weights_epoch_0.pth --save=./weights/ --batch_s 100 --batch_t 1000Batch sizes have to be big according to the paper, to keep a good distribution of labels/samples (e.g. 100 per class)
python domain_adaptation.py --save=./weights/ --batch_s 100 --batch_t 1000
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