In this project, I am working on Oxford dataset and doing image retrieval. I am using a Siamese kind of network. I am trying different kind of loss. The starter code was taken from https://github.com/harveyslash/Facial-Similarity-with-Siamese-Networks-in-Pytorch and changed as needed
CAL101 : Contrastive best : savemodel/CAL101/netconv99.pth Using 3 number of neighbours
precision recall f1-score support
avg / total 0.82 0.75 0.76 1709
Accuracy = 0.753657109421
CAL101 : Dot Product best : savemodel/CAL101dot/netconv68.pth Using 6 number of neighbours
precision recall f1-score support
avg / total 0.88 0.83 0.84 1709
Accuracy = 0.832650672908
CAL101 : Neural best : savemodel/CAL101neural/netconv20.pth
precision recall f1-score support
avg / total 0.30 0.26 0.25 1709
Accuracy = 0.256290228204
CAL256 : Contrastive best : savemodel/CAL256/netconv104.pth Using 9 number of neighbours
precision recall f1-score support
avg / total 0.50 0.41 0.43 5942
Accuracy = 0.407943453383
CAL256 : dot product loss best : savemodel/CAL256dot/netconv63.pth Using 9 number of neighbours
precision recall f1-score support
avg / total 0.53 0.50 0.50 5942
Accuracy = 0.503702457085
CAL256 : neural loss best : savemodel/CAL256neural/netconv65.pth Using 3 number of neighbours
precision recall f1-score support
avg / total 0.11 0.10 0.09 5942
Accuracy = 0.100134634803
The best accuracy for the IIA30 dataset (+ other samples) was with contrastive divergence : savemodel/contrasMIXEDsimpledata/netconv22.pth
| precision | recall | f1 score | support |
|---|---|---|---|
| avg / total | 0.99 | 0.97 | 0.98 |
Accuracy = 0.973684210526
Best accuracy for dot loss: savemodel/newdatadotprod/netconv53.pth
| precision | recall | f1-score | support |
|---|---|---|---|
| avg / total | 1.00 | 0.97 | 0.98 |
Accuracy = 0.973684210526
Best accuracy for neural loss: savemodel/newdataneural/netconv61.pth
| precision | recall | f1-score | support |
|---|---|---|---|
| avg / total | 0.80 | 0.75 | 0.74 |
Accuracy = 0.745614035088
