Deep Metric Learning in PyTorch

 Learn a deep metric which can be used image 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]

• Distance Weighted Sampling [5]

• NCA Loss [6]

-WeightLoss My own loss (not public now)

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).

Prerequisites

Requirements

• Python >= 3.5
• PyTorch >= 0.4
• tqdm (Optional for test.py)
• tensorboard >= 1.7.0 (Optional for TensorboardX)
• tensorboardX >= 1.2 (Optional for TensorboardX)

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
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

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
weight	78.18	90.47	96.0	98.74	89.64	97.87	98.47	98.84	99.05	99.20

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

*** weight :***

sh run_train_00.sh

TODOs

• Use SGD to instead of Adam
• Update the loss to PyTorch 1.0 version
• tensorboardX Visualization
• Evalate models during training
• Multi-GPU support
• using config file (json or yaml instead of all parameters in bash)

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.]