Metric learning models in PyTorch, recall@1

	CUB2011	CARS196	Stanford Online Products
Margin contrastive loss, semi-hard	0.58 @ epoch60	0.80 @ epoch60	0.7526 @ epoch90
Lifted structured embedding
Triplet loss

Original impl of Margin contrastive loss published at: https://github.com/apache/incubator-mxnet/tree/19ede063c4756fa49cfe741e654180aee33991c6/example/gluon/embedding_learning (temporarily removed in apache/mxnet#20602)

Examples

# evaluation results are saved in ./data/log.txt

# train margin contrastive loss on CUB2011 using ResNet-50
python train.py --dataset cub2011 --model margin --base resnet50

# download GoogLeNet weights and train using LiftedStruct loss
wget -P ./data https://github.com/vadimkantorov/metriclearningbench/releases/download/data/googlenet.h5
python train.py --dataset cub2011 --model liftedstruct --base inception_v1_googlenet

# evaluate raw final layer embeddings on CUB2011 using ResNet-50
python train.py --dataset cub2011 --model untrained  --epochs 1