SphericalEmbedding

This repository is the official implementation of [Deep Metric Learning with Spherical Embedding] on deep metric learning (DML) task.

📋 Training a vanilla triplet loss / semihard triplet loss / normalized N-pair loss (tuplet loss) / multi-similarity loss on CUB200-2011 / Cars196 / SOP / In-Shop datasets.

Requirements

This repo was tested with Ubuntu 16.04.1 LTS, Python 3.6, PyTorch 1.1.0, and CUDA 10.1.

Requirements: torch==1.1.0, tensorboardX

Training

1. Prepare datasets and pertained BN-Inception.

   Download datasets: CUB200-2011, Cars196, SOP, In-Shop, unzip and organize them as follows.

└───datasets
    └───split_train_test.py
    └───CUB_200_2011
    |   └───images.txt
    |   └───images
    |       └───001.Black_footed_Albatross
    |       └───...
    └───CARS196
    |   └───cars_annos.mat
    |   └───car_ims
    |       └───000001.jpg
    |       └───...
    └───SOP
    |   └───Stanford_Online_Products
    |       └───Ebay_train.txt
    |       └───Ebay_test.txt
    |       └───bicycle_final
    |       └───...
    └───Inshop
    |   └───list_eval_partition.txt
    |   └───img
    |       └───MEN
    |       └───WOMEN
    |       └───...

​ Then run split_train_test.py to generate training and testing lists.

​ Download the imagenet pertained BN-Inception and put it into ./pretrained_models.

2. To train the model(s) in the paper, run the following commands or use sh mytrain.sh.

   Train models with vanilla triplet loss.

CUDA_VISIBLE_DEVICES=0 python train.py --use_dataset CUB --instances 3 --lr 0.5e-5 --lr_p 0.25e-5 \
--lr_gamma 0.1 --use_loss triplet

​ Train models with vanilla triplet loss + SEC.

CUDA_VISIBLE_DEVICES=0 python train.py --use_dataset CUB --instances 3 --lr 0.5e-5 --lr_p 0.25e-5 \
--lr_gamma 0.1 --use_loss triplet --sec_wei 1.0

​ Train models with vanilla triplet loss + L2-reg.

CUDA_VISIBLE_DEVICES=0 python train.py --use_dataset CUB --instances 3 --lr 0.5e-5 --lr_p 0.25e-5 \
--lr_gamma 0.1 --use_loss triplet --l2reg_wei 1e-4

​ Similarly, we set --use_loss to semihtriplet/n-npair/ms and --instances to 3/2/5, for training models with semihard triplet loss / normalized N-pair loss / multi-similarity loss. We set --use_dataset to Cars/SOP/Inshop, for training models on other datasets.

📋 The detailed settings of the above hyper-parameters is provided in Appendix B of our paper (with two exceptions to the lr settings listed below).

(a) multi-similarity loss without SEC/L2-reg on CUB: 1e-5/0.5e-5/0.1@3k, 6k

(b) multi-similarity loss without SEC/L2-reg on Cars: 2e-5/2e-5/0.1@2k

(We find that using a larger learning rate harms the original loss function.)

When training on a different dataset or with a different loss function, we only need to modify the hyper-parameters in above commands and the head settings (only when using multi-similarity loss without SEC/L2-reg, we need to set need_bn=False,

self.model = torch.nn.DataParallel(BNInception(need_bn=False)).cuda()

in line 24 of learner.py).

📋 Additionally, to use SEC with EMA method, we need to set --norm_momentum <value>, where norm_momentum denotes $\rho$ in Appendix D of our paper.

Testing

The test of NMI and F1 on SOP costs a lot of time, and we thus conduct it only after the training process (we only conduct test of R@K during training). In particular, run:

CUDA_VISIBLE_DEVICES=0 python test_sop.py --use_dataset SOP --test_sop_model SOP_xxxx_xxxx

or use sh test_sop.sh for a complete test of NMI, F1, and R@K on SOP. Here SOP_xxxx_xxxx is the model to be tested which could be found in ./work_space.

For other three datasets, the test of NMI, F1, and R@K is conducted during the training process.

Results

Our model achieves the following performance on CUB200-2011, Cars196, SOP, and In-Shop datasets:

Citation

If you find this repo useful for your research, please consider citing this paper

@inproceedings{
}