/Deep_metric

Deep Metric Learning

Primary LanguagePython

Deep Metric Learning in PyTorch

 Learn deep metric for image retrieval or other information retrieval.

Deep metric methods implemented in this repositories:

  • Contrasstive Loss [1]

  • Semi-Hard Mining Strategy [2]

  • Lifted Structure Loss* [3] (Modified version because of its original weak performance)

  • Binomial BinDeviance Loss [4]

  • NCA Loss [6]

Dataset

  • Car-196

    first 98 classes as train set and last 98 classes as test set

  • CUB-200-2011

    first 100 classes as train set and last 100 classes as test set

  • 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

  • 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).

  • Processed CUB and Cars196

    Extract code: inmj

    To easily reimplement the performance, I provide the processed datasets: CUB and Cars-196.

Requirements

  • Python >= 3.5
  • PyTorch = 1.0

Comparasion with state-of-the-art on CUB-200 and Cars-196

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

Comparasion with state-of-the-art on SOP and In-shop

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

see more detail in our CVPR-2019 paper Multi-Similarity Loss

Reproducing Car-196 (or CUB-200-2011) experiments

*** weight :***

sh run_train_00.sh

References

[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.]