A PyTorch Implementation for Sketch Triplet Networks.
- Optimizer
- Adam
- shoes
- chairs
| 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 branch | pretrained | Loss Function | prec | mprec |
|---|---|---|---|---|
| AlexNet | T | TripletMarginLoss | ||
| ResNet18 | T | MarginRankingLoss | ||
| ResNet18 | T(ImageNet) | TripletMarginLoss | ||
| ResNet18 | T(TU-Berlin) | TripletMarginLoss |
| 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 |
| 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 |
| Model branch | pretrained | Loss Function | prec | mprec |
|---|---|---|---|---|
| SketchANet | TU-Berlin | Triplet Loss | 74.46 | 51.09 |
| 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]
-
- Sketch Me That Shoe
- Deep Spatial-Semantic Attention for Fine-Grained Sketch-Based Image Retrieval