Learn deep metric for image retrieval or other information retrieval.
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Contrasstive Loss [1]
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Semi-Hard Mining Strategy [2]
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Lifted Structure Loss* [3] (Modified version because of its original weak performance)
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Binomial BinDeviance Loss [4]
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Distance Weighted Sampling [5] (deleted for poor performance)
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NCA Loss [6]
-WeightLoss (accepted by CVPR2019, will be public soon.)
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first 98 classes as train set and last 98 classes as test set
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first 100 classes as train set and last 100 classes as test set
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Stanford-Online-Products
for the experiments, we split 59,551 images of 11,318 classes for training and 60,502 images of 11,316 classes for testing
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In-Shop-clothes-Retrieval
For the In-Shop Clothes Retrieval dataset, 3,997 classes with 25,882 images for training. And the test set are partitioned to query set with 3,985 classes(14,218 images) and gallery set with 3,985 classes (12,612 images).
- Python >= 3.5
- PyTorch >= 0.4
- tqdm (Optional for
test.py) - tensorboard >= 1.7.0 (Optional for TensorboardX)
- tensorboardX >= 1.2 (Optional for TensorboardX)
| Recall@K | 1 | 2 | 4 | 8 | 16 | 32 | 1 | 2 | 4 | 8 | 16 | 32 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| HDC | 53.6 | 65.7 | 77.0 | 85.6 | 91.5 | 95.5 | 73.7 | 83.2 | 89.5 | 93.8 | 96.7 | 98.4 |
| Clustering | 48.2 | 61.4 | 71.8 | 81.9 | - | - | 58.1 | 70.6 | 80.3 | 87.8 | - | - |
| ProxyNCA | 49.2 | 61.9 | 67.9 | 72.4 | - | - | 73.2 | 82.4 | 86.4 | 87.8 | - | - |
| Smart Mining | 49.8 | 62.3 | 74.1 | 83.3 | - | - | 64.7 | 76.2 | 84.2 | 90.2 | - | - |
| Margin [5] | 63.6 | 74.4 | 83.1 | 90.0 | 94.2 | - | 79.6 | 86.5 | 91.9 | 95.1 | 97.3 | - |
| HTL | 57.1 | 68.8 | 78.7 | 86.5 | 92.5 | 95.5 | 81.4 | 88.0 | 92.7 | 95.7 | 97.4 | 99.0 |
| ABIER | 57.5 | 68.7 | 78.3 | 86.2 | 91.9 | 95.5 | 82.0 | 89.0 | 93.2 | 96.1 | 97.8 | 98.7 |
| Weight | 66.85 | 77.84 | 85.8 | 91.29 | 94.94 | 97.42 | 83.69 | 90.27 | 94.53 | 97.16 | 98.65 | 99.36 |
| Recall@K | 1 | 10 | 100 | 1000 | 1 | 10 | 20 | 30 | 40 | 50 |
|---|---|---|---|---|---|---|---|---|---|---|
| Clustering | 67.0 | 83.7 | 93.2 | - | - | - | - | - | - | - |
| HDC | 69.5 | 84.4 | 92.8 | 97.7 | 62.1 | 84.9 | 89.0 | 91.2 | 92.3 | 93.1 |
| Margin [5] | 72.7 | 86.2 | 93.8 | 98.0 | - | - | - | - | - | - |
| Proxy-NCA | 73.7 | - | - | - | - | - | - | - | - | - |
| ABIER | 74.2 | 86.9 | 94.0 | 97.8 | 83.1 | 95.1 | 96.9 | 97.5 | 97.8 | 98.0 |
| HTL | 74.8 | 88.3 | 94.8 | 98.4 | 80.9 | 94.3 | 95.8 | 97.2 | 97.4 | 97.8 |
| weight | 78.18 | 90.47 | 96.0 | 98.74 | 89.64 | 97.87 | 98.47 | 98.84 | 99.05 | 99.20 |
*** weight :***
sh run_train_00.sh- Use SGD to instead of Adam
- Update the loss to PyTorch 1.0 version
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tensorboardXVisualization - Evalate models during training
- Multi-GPU support
- using config file (json or yaml instead of all parameters in bash)
[1] [R. Hadsell, S. Chopra, and Y. LeCun. Dimensionality reduction by learning an invariant mapping]
[2] [F. Schroff, D. Kalenichenko, and J. Philbin. Facenet: A unified embedding for face recognition and clustering. In CVPR, 2015.]
[3][H. Oh Song, Y. Xiang, S. Jegelka, and S. Savarese. Deep metric learning via lifted structured feature embedding. In CVPR, 2016.]
[4][D. Yi, Z. Lei, and S. Z. Li. Deep metric learning for practical person re-identification.]
[5][C. Wu, R. Manmatha, A. J. Smola, and P. Kr¨ahenb¨uhl. Sampling matters in deep embedding learning. ICCV, 2017.]
[6][R. Salakhutdinov and G. Hinton. Learning a nonlinear embedding by preserving class neighbourhood structure. In AISTATS, 2007.]