MixBCT: Towards Self-Adapting Backward-Compatible Training

Workflow	Performance(Open-Class scenario)

Constraints

Introduction

Implementation of MixBCT: Towards Self-Adapting Backward-Compatible Training(Ours) , L2 and other SOTA methods: UniBCT, NCCL, BCT, AdvBCT

L2: Conduct simple L2 constraint between old features and new features

BCT: Towards Backward-Compatible Representation Learning (CVPR2020)

UniBCT: Towards Universal Backward-Compatible Representation Learning (IJCAI2022)

NCCL: Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation (AAAI2022)

AdvBCT: Boundary-Aware Backward-Compatible Representation via Adversarial Learning in Image Retrieval (CVPR2023)

Dataset

• Training dataset: MS1M-V3 (ms1m-retinaface) ---- 5179510 images with 93431 IDs
• Eval dataset: IJB-C

The download link of the datasets can be find in [https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_]

Code Structure

• The main-dir(./) is used for train the Old model
• ./BCT_methods/ --- The methods which summarized MixBCT, UniBCT, NCCL, BCT, AdvBCT and L2.
• ./tools/ --- dataset split code, some preprocessing operations code and IJB-C evaluation code

This code based on the project insightface, We maintain separate directories for each method to enhance clarity and facilitate reproducibility.

Note: We fixed the random seed in the main file for training, and this will significantly reduce the speed of training. You can speed up the training by comment out following two lines in the main file:

#torch.backends.cudnn.benchmark = False
#torch.backends.cudnn.deterministic = True

However, it will result in slight randomness of the results.

Training Flow ---- An Example:

Step-1

Train the old model use the arcface loss.

python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1  --master_port=22222 train_old_arc.py configs/f512_r18_arc_class30.py

Or train the old model use the softmax loss.

python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1  --master_port=22222 train_old_softmax.py configs/f128_r18_softmax_class30.py

Step-2 ① ----（preprocessing operations）used in MixBCT、NCCL、AdvBCT

Get the old features of the dataset consist of 'class70'(the sub-dataset containing 70 percent of the classes) images.

python tools/get_feature/get_feature.py configs/f128_r18_softmax_class30.py --SD f128_r18_softmax_class70

Step-2 ----（preprocessing operations）used in MixBCT

Get the old denoised feature of the dataset consist of 'class70' images(based on ①).

python tools/get_feature/denoise_credible.py --T 0.9 --SD f128_r18_softmax_class70

Step-2 ----（preprocessing operations）used in BCT、UniBCT

Get the old average feature of the dataset consist of 'class70' images(based on ①).

python tools/get_feature/get_avg_feature.py  --SD f128_r18_softmax_class70

Step-3

Train the new model by MixBCT

cd BCT_Methods/MixBCT/
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1  --master_port=22222 train.py configs/OPclass_ms1mv3_r18_to_r50_MixBCT_softmax_to_arc_f128.py

Or train the new model by NCCL

cd BCT_Methods/NCCL/
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1  --master_port=22222 train.py configs/OPclass_ms1mv3_r18_to_r50_NCCL_softmax_to_arc_f128.py

Or train the new model by other methods

cd BCT_Methods/#Other Methods/
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1  --master_port=22222 train.py configs/OPclass_ms1mv3_r18_to_r50_(Othermethods)_softmax_to_arc_f128.py

Step-4

IJB-C evaluation

**self-test 1:1**
python tools/ijbc_eval/ijbc_eval.py -m=#The path of 'New_model.pt' -net=#The backbone of Nld model(r18,r50,vit...) 
**self-test 1:N**
python tools/ijbc_eval/ijbc_eval.py -m=#The path of 'New_model.pt' -net=#The backbone of Nld model(r18,r50,vit...) -N 
**cross-test 1:1**
python tools/ijbc_eval/ijbc_eval.py -m=#The path of 'New_model.pt' -net=#The backbone of Nld model(r18,r50,vit...) -m_old=#The path of 'Old_model.pt' -old_net=#The backbone of Old model(r18,r50,vit...) 
**cross-test 1:N**
python tools/ijbc_eval/ijbc_eval.py -m=#The path of 'New_model.pt' -net=#The backbone of Nld model(r18,r50,vit...) -m_old=#The path of 'Old_model.pt' -old_net=#The backbone of Old model(r18,r50,vit...) 

Implementation Details

We use 8 NVIDIA 2080Ti/3090Ti GPUs for training and apply automatic mixed precision (AMP) with float16 and float32. We use standard stochastic gradient descent (SGD) as the optimizer. The random seed is set to 666. Batch-size is set to 128 × 8. An initial learning rate of 0.1, and the learning rate linearly decays to zero over the course of training. The weight decay is set to 5×10^{-4} and momentum is 0.9. The training stops after 35 epochs. We set the ratio α of old and new features in the mixup process to 0.3. Moreover, the λ in L2 regression loss is set to 10 to match loss scale with the classfication loss.

Citation

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