Skip to content

Latest commit

 

History

History
76 lines (63 loc) · 4.81 KB

README.md

File metadata and controls

76 lines (63 loc) · 4.81 KB

Sketch Triplet Networks.

A PyTorch Implementation for Sketch Triplet Networks.

Model Configuration

  • Optimizer
    • Adam

DataSet

  • shoes
  • chairs

Model Parameters

Model branch pretrained Loss Function lr clip_grad_norm(max_norm) learning rate decay weight_decay Margin
SketchANet TU-Berlin Triplet Loss 2e-5 -- 20 0.0005(shoes) 0.0005-0.001(chairs) 0.3
AlexNet T(ImageNet) 2e-4 1.0 100 0.0003 0.3
ResNet18 T MarginRankingLoss 2e-6 10.0 20 0.05 0.3
ResNet18 T(TU-Berlin) TripletMarginLoss 2e-6 10.0 20 0.01 0.3

Model Result

Shoes

Train Set(ranking)

Model branch pretrained Loss Function prec mprec
AlexNet T TripletMarginLoss
ResNet18 T MarginRankingLoss
ResNet18 T(ImageNet) TripletMarginLoss
ResNet18 T(TU-Berlin) TripletMarginLoss

Test Set(ranking)

Model branch pretrained Loss Function prec mprec
SketchANet TU-Berlin Triplet Loss
AlexNet T TripletMarginLoss 61.76 15.34
ResNet18 T MarginRankingLoss
ResNet18 T(ImageNet) TripletMarginLoss
ResNet18 T(TU-Berlin) TripletMarginLoss

Test Set(retrieval)

Model branch pretrained Loss Function Rank@1 Rank@5 Rank@10 corr
Origini Triplet Loss 39.13 -- 87.83 69.49
Originii ImageNet(edge)+TU-Berlin Triplet Loss(square_distance) 52.174 -- 92.174 --
SketchANet TU-Berlin Triplet Loss 45.217 77.391 82.609 72.15
AlexNet ImageNet+TU-Berlin Triplet Loss 45.217 74.783 86.087 73.70
AlexNet T TripletMarginLoss
ResNet18 TU-Berlin TripletLoss 26.957 51.304 64.348 64.54
ResNet18 T MarginRankingLoss
ResNet18 TU-Berlin MarginRankingLoss 29.565 50.435 69.565 64.21
ResNet18 ImageNet TripletMarginLoss
ResNet18 TU-Berlin TripletMarginLoss 25.217 53.043 65.217 64.79
ResNet18 TU-Berlin TripletMarginLoss + embedded_norm

Chairs

Train Set(ranking)

Test Set(ranking)

Model branch pretrained Loss Function prec mprec
SketchANet TU-Berlin Triplet Loss 74.46 51.09

Test Set(retrieval)

Model branch pretrained Loss Function Rank@1 Rank@5 Rank@10 corr
Origini Triplet Loss 69.07 -- 97.94 72.30
Originii ImageNet(edge)+TU-Berlin Triplet Loss(square_distance) 72.16 -- 98.96 --
SketchANet TU-Berlin Triplet Loss 76.289 91.753 92.784 73.45
AlexNet ImageNet+TU-Berlin Triplet Loss 63.918 87.629 92.784 73.13
ResNet18 ImageNet+TU-Berlin Triplet Loss 61.856 87.629 93.814 76.01

[Reference]

    1. Sketch Me That Shoe
    2. Deep Spatial-Semantic Attention for Fine-Grained Sketch-Based Image Retrieval