This repository contains all code and implementations used in:

Revisiting Training Strategies and Generalization Performance in Deep Metric Learning

accepted to ICML 2020.

Link: https://arxiv.org/abs/2002.08473

The code is meant to serve as a research starting point in Deep Metric Learning. By implementing key baselines under a consistent setting and logging a vast set of metrics, it should be easier to ensure that method gains are not due to implementational variations, while better understanding driving factors.

It is set up in a modular way to allow for fast and detailed prototyping, but with key elements written in a way that allows the code to be directly copied into other pipelines. In addition, multiple training and test metrics are logged in W&B to allow for easy and large-scale evaluation.

Finally, please find a public W&B repo with key runs performed in the paper here: https://app.wandb.ai/confusezius/RevisitDML.

Contact: Karsten Roth, [email protected]

Suggestions are always welcome!

If you use this code in your research, please cite

@misc{roth2020revisiting,
    title={Revisiting Training Strategies and Generalization Performance in Deep Metric Learning},
    author={Karsten Roth and Timo Milbich and Samarth Sinha and Prateek Gupta and Björn Ommer and Joseph Paul Cohen},
    year={2020},
    eprint={2002.08473},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

This repository contains (in parts) code that has been adapted from:

• https://github.com/idstcv/SoftTriple
• https://github.com/bnu-wangxun/Deep_Metric
• https://github.com/valerystrizh/pytorch-histogram-loss
• https://github.com/Confusezius/Deep-Metric-Learning-Baselines

Make sure to also check out the following repo with a great plug-and-play implementation of DML methods:

• https://github.com/KevinMusgrave/pytorch-metric-learning

All implemented methods and metrics are listed at the bottom!

• ALL standardized Runs that were used are available in Revisit_Runs.sh.
• These runs are also logged in this public W&B repo: https://app.wandb.ai/confusezius/RevisitDML.
• All Runs and their respective metrics can be downloaded and evaluated to generate the plots in our paper by following Result_Evaluations.py. This also allows for potential introspection of other relations. It also converts results directly into Latex-table format with mean and standard deviations.
• To utilize different batch-creation methods, simply set the flag --data_sampler to the method of choice. Allowed flags are listed in datasampler/__init__.py.
• To use the proposed spectral regularization for tuple-based methods, set --batch_mining rho_distance with flip probability --miner_rho_distance_cp e.g. 0.2.
• A script to run the toy experiments in the paper is provided in toy_experiments.

Note: There may be small deviations in results based on the Hardware (e.g. between P100 and RTX GPUs) and Software (different PyTorch/Cuda versions) used to run these experiments, but they should be covered in the standard deviations reported in the paper.

• PyTorch 1.2.0+ & Faiss-Gpu
• Python 3.6+
• pretrainedmodels, torchvision 0.3.0+

An exemplary setup of a virtual environment containing everything needed:

(1) wget  https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
(2) bash Miniconda3-latest-Linux-x86_64.sh (say yes to append path to bashrc)
(3) source .bashrc
(4) conda create -n DL python=3.6
(5) conda activate DL
(6) conda install matplotlib scipy scikit-learn scikit-image tqdm pandas pillow
(7) conda install pytorch torchvision faiss-gpu cudatoolkit=10.0 -c pytorch
(8) pip install wandb pretrainedmodels
(9) Run the scripts!

Data for

• CUB200-2011 (http://www.vision.caltech.edu/visipedia/CUB-200.html)
• CARS196 (https://ai.stanford.edu/~jkrause/cars/car_dataset.html)
• Stanford Online Products (http://cvgl.stanford.edu/projects/lifted_struct/)

can be downloaded either from the respective project sites or directly via Dropbox:

• CUB200-2011 (1.08 GB): https://www.dropbox.com/s/tjhf7fbxw5f9u0q/cub200.tar?dl=0
• CARS196 (1.86 GB): https://www.dropbox.com/s/zi2o92hzqekbmef/cars196.tar?dl=0
• SOP (2.84 GB): https://www.dropbox.com/s/fu8dgxulf10hns9/online_products.tar?dl=0

The latter ensures that the folder structure is already consistent with this pipeline and the dataloaders.

Otherwise, please make sure that the datasets have the following internal structure:

• For CUB200-2011/CARS196:

cub200/cars196
└───images
|    └───001.Black_footed_Albatross
|           │   Black_Footed_Albatross_0001_796111
|           │   ...
|    ...

• For Stanford Online Products:

online_products
└───images
|    └───bicycle_final
|           │   111085122871_0.jpg
|    ...
|
└───Info_Files
|    │   bicycle.txt
|    │   ...

Assuming your folder is placed in e.g. <$datapath/cub200>, pass $datapath as input to --source.

Training is done by using main.py and setting the respective flags, all of which are listed and explained in parameters.py. A vast set of exemplary runs is provided in Revisit_Runs.sh.

[I.] A basic sample run using default parameters would like this:

python main.py --loss margin --batch_mining distance --log_online \
              --project DML_Project --group Margin_with_Distance --seed 0 \
              --gpu 0 --bs 112 --data_sampler class_random --samples_per_class 2 \
              --arch resnet50_frozen_normalize --source $datapath --n_epochs 150 \
              --lr 0.00001 --embed_dim 128 --evaluate_on_gpu

The purpose of each flag explained:

• --loss <loss_name>: Name of the training objective used. See folder criteria for implementations of these methods.
• --batch_mining <batchminer_name>: Name of the batch-miner to use (for tuple-based ranking methods). See folder batch_mining for implementations of these methods.
• --log_online: Log metrics online via either W&B (Default) or CometML. Regardless, plots, weights and parameters are all stored offline as well.
• --project, --group: Project name as well as name of the run. Different seeds will be logged into the same --group online. The group as well as the used seed also define the local savename.
• --seed, --gpu, --source: Basic Parameters setting the training seed, the used GPU and the path to the parent folder containing the respective Datasets.
• --arch: The utilized backbone, e.g. ResNet50. You can append _frozen and _normalize to the name to ensure that BatchNorm layers are frozen and embeddings are normalized, respectively.
• --data_sampler, --samples_per_class: How to construct a batch. The default method, class_random, selects classes at random and places <samples_per_class> samples into the batch until the batch is filled.
• --lr, --n_epochs, --bs ,--embed_dim: Learning rate, number of training epochs, the batchsize and the embedding dimensionality.
• --evaluate_on_gpu: If set, all metrics are computed using the gpu - requires Faiss-GPU and may need additional GPU memory.

• During training, metrics listed in --evaluation_metrics will be logged for both training and validation/test set. If you do not care about detailed training metric logging, simply set the flag --no_train_metrics. A checkpoint is saved for improvements in metrics listed in --storage_metrics on training, validation or test sets. Detailed information regarding the available metrics can be found at the bottom of this README.
• If one wishes to use a training/validation split, simply set --use_tv_split and --tv_split_perc <train/val split percentage>.

[II.] Advanced Runs:

python main.py --loss margin --batch_mining distance --loss_margin_beta 0.6 --miner_distance_lower_cutoff 0.5 ... (basic parameters)

• To use specific parameters that are loss, batchminer or e.g. datasampler-related, simply set the respective flag.
• For structure and ease of use, parameters relating to a specifc loss function/batchminer etc. are marked as e.g. --loss_<lossname>_<parameter_name>, see parameters.py.
• However, every parameter can be called from every class, as all parameters are stored in a shared namespace that is passed to all methods. This makes it easy to create novel fusion losses and the likes.

Here some information on using W&B (highly encouraged!)

• Create an account here (free): https://wandb.ai
• After the account is set, make sure to include your API key in parameters.py under --wandb_key.
• To make sure that W&B data can be stored, ensure to run wandb on in the folder pointed to by --save_path.
• When data is logged online to W&B, one can use Result_Evaluations.py to download all data, create named metric and correlation plots and output a summary in the form of a latex-ready table with mean and standard deviations of all metrics. This ensures that there are no errors between computed and reported results.

1. Create custom objectives: Simply take a look at e.g. criteria/margin.py, and ensure that the used methods has the following properties:

• Inherit from torch.nn.Module and define a custom forward() function.
• When using trainable parameters, make sure to either provide a self.lr to set the learning rate of the loss-specific parameters, or set self.optim_dict_list, which is a list containing optimization dictionaries passed to the optimizer (see e.g criteria/proxynca.py). If both are set, self.optim_dict_list has priority.
• Depending on the loss, remember to set the variables ALLOWED_MINING_OPS = None or list of allowed mining operations, REQUIRES_BATCHMINER = False or True, REQUIRES_OPTIM = False or True to denote if the method needs a batchminer or optimization of internal parameters.

2. Create custom batchminer: Simply take a look at e.g. batch_mining/distance.py - The miner needs to be a class with a defined __call__()-function, taking in a batch and labels and returning e.g. a list of triplets.

3. Create custom datasamplers:Simply take a look at e.g. datasampler/class_random_sampler.py. The sampler needs to inherit from torch.utils.data.sampler.Sampler and has to provide a __iter__() and a __len__() function. It has to yield a set of indices that are used to create the batch.

For a detailed explanation of everything, please refer to the supplementary of our paper!

• Angular [Deep Metric Learning with Angular Loss] --loss angular
• ArcFace [ArcFace: Additive Angular Margin Loss for Deep Face Recognition] --loss arcface
• Contrastive [Dimensionality Reduction by Learning an Invariant Mapping] --loss contrastive
• Generalized Lifted Structure [In Defense of the Triplet Loss for Person Re-Identification] --loss lifted
• Histogram [Learning Deep Embeddings with Histogram Loss] --loss histogram
• Marginloss [Sampling Matters in Deep Embeddings Learning] --loss margin
• MultiSimilarity [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning] --loss multisimilarity
• N-Pair [Improved Deep Metric Learning with Multi-class N-pair Loss Objective] --loss npair
• ProxyNCA [No Fuss Distance Metric Learning using Proxies] --loss proxynca
• Quadruplet [Beyond triplet loss: a deep quadruplet network for person re-identification] --loss quadruplet
• Signal-to-Noise Ratio (SNR) [Signal-to-Noise Ratio: A Robust Distance Metric for Deep Metric Learning] --loss snr
• SoftTriple [SoftTriple Loss: Deep Metric Learning Without Triplet Sampling] --loss softtriplet
• Normalized Softmax [Classification is a Strong Baseline for Deep Metric Learning] --loss softmax
• Triplet [Facenet: A unified embedding for face recognition and clustering] --loss triplet

• Random [Facenet: A unified embedding for face recognition and clustering] --batch_mining random
• Semihard [Facenet: A unified embedding for face recognition and clustering] --batch_mining semihard
• Softhard [https://github.com/Confusezius/Deep-Metric-Learning-Baselines] --batch_mining softhard
• Distance-based [Sampling Matters in Deep Embeddings Learning] --batch_mining distance
• Rho-Distance [Revisiting Training Strategies and Generalization Performance in Deep Metric Learning] --batch_mining rho_distance
• Parametric [PADS: Policy-Adapted Sampling for Visual Similarity Learning] --batch_mining parametric

• ResNet50 [Deep Residual Learning for Image Recognition] e.g. --arch resnet50_frozen_normalize.
• Inception-BN [Going Deeper with Convolutions] e.g. --arch bninception_normalize_frozen.
• GoogLeNet (torchvision variant w/ BN) [Going Deeper with Convolutions] e.g. --arch googlenet.

• CUB200-2011 [Caltech-UCSD Birds-200-2011] --dataset cub200.
• CARS196 [Cars Dataset] --dataset cars196.
• Stanford Online Products [Deep Metric Learning via Lifted Structured Feature Embedding] --dataset online_products.

Metrics based on Euclidean Distances

• Recall@k: Include R@1 e.g. with e_recall@1 into the list of evaluation metrics --evaluation_metrics.
• Normalized Mutual Information (NMI): Include with nmi.
• F1: include with f1.
• mAP (class-averaged): Include standard mAP at Recall with mAP_lim. You may also include mAP_1000 for mAP limited to Recall@1000, and mAP_c limited to mAP at Recall@Max_Num_Samples_Per_Class. Note that all of these are heavily correlated.

Metrics based on Cosine Similarities (not included by default)

• Cosine Recall@k: Cosine-Similarity variant of Recall@k. Include with c_recall@k in --evaluation_metrics.
• Cosine Normalized Mutual Information (NMI): Include with c_nmi.
• Cosine F1: include with c_f1.
• Cosine mAP (class-averaged): Include cosine similarity mAP at Recall variants with c_mAP_lim. You may also include c_mAP_1000 for mAP limited to Recall@1000, and c_mAP_c limited to mAP at Recall@Max_Num_Samples_Per_Class.

Embedding Space Metrics

• Spectral Variance: This metric refers to the spectral decay metric used in our ICML paper. Include it with rho_spectrum@1. To exclude the k largest spectral values for a more robust estimate, simply include rho_spectrum@k+1. Adding rho_spectrum@0 logs the whole singular value distribution, and rho_spectrum@-1 computes KL(q,p) instead of KL(p,q).
• Mean Intraclass Distance: Include the mean intraclass distance via dists@intra.
• Mean Interclass Distance: Include the mean interlcass distance via dists@inter.
• Ratio Intra- to Interclass Distance: Include the ratio of distances via dists@intra_over_inter.