Code for CVPR2019 paper《Unequal Training for Deep Face Recognition with Long Tailed Noisy Data》

Overview

Unequal-Training-for-Deep-Face-Recognition-with-Long-Tailed-Noisy-Data.

This is the code of CVPR 2019 paper《Unequal Training for Deep Face Recognition with Long Tailed Noisy Data》.

arch

Usage Instructions

  1. The code is adopted from InsightFace. I sincerely appreciate for their contributions.

  2. Our method need two stage training, therefore the code is also stepwise. I will be happy if my humble code would help you. If there are questions or issues, please let me know.

Note:

  1. Our method is appropriate for the noisy data with long-tailed distribution such as MF2 training dataset. When the training data is good, like MS1M and VGGFace2, InsightFace is more suitable.

  2. We use the last arcface model (best performance) to find the third type noise. Next we drop the fc weight of the last arcface model, then finetune from it using NR loss (adding a reweight term by putting more confidence in the prediction of the training model).

  3. The second stage training process need very careful manual tuning. We provide our training log for reference.

Prepare the code and the data.

  1. Install MXNet with GPU support (Python 2.7).
pip install mxnet-cu90
  1. download the code as unequal_code/
git clone https://github.com/zhongyy/Unequal-Training-for-Deep-Face-Recognition-with-Long-Tailed-Noisy-Data.git
  1. download the MF2 training dataset(password: w9y5) and the evaluation dataset, then place them in unequal_code/MF2_pic9_head/ unequal_code/MF2_pic9_tail/ and unequal_code/eval_dataset/ respectively.

step 1: Pretrain MF2_pic9_head with ArcFace.

End it when the acc of validation dataset (lfw,cfp-fp and agedb-30) does not ascend.

CUDA_VISIBLE_DEVICES='0,1' python -u train_softmax.py --network r50 --loss-type 4  --margin-m 0.5 --data-dir ./MF2_pic9_head/ --end-epoch 40 --per-batch-size 100 --prefix ../models/r50_arc_pic9/model 2>&1|tee r50_arc_pic9.log

step 2: Train the head data with NRA (finetune from step 1).

  1. Once the model_t,0 is saved, end it.
CUDA_VISIBLE_DEVICES='0,1' python -u train_NR_savemodel.py --network r50 --loss-type 4 --margin-m 0.5 --data-dir ./MF2_pic9_head/ --end-epoch 1 --lr 0.01  --per-batch-size 100 --noise-beta 0.9 --prefix ../models/NRA_r50pic9/model_t --bin-dir ./src/ --pretrained ../models/r50_arc_pic9/model,xx 2>&1|tee NRA_r50pic9_savemodel.log
  1. End it when the acc of validation dataset(lfw, cfp-fp and agedb-30) does not ascend.
CUDA_VISIBLE_DEVICES='0,1' python -u train_NR.py --network r50 --loss-type 4 --margin-m 0.5 --data-dir ./MF2_pic9_head/ --lr 0.01 --lr-steps 50000,90000 --per-batch-size 100 --noise-beta 0.9 --prefix ../models/NRA_r50pic9/model --bin-dir ./src/ --pretrained ../models/NRA_r50pic9/model_t,0 2>&1|tee NRA_r50pic9.log

step 3:

  1. Generate the denoised head data using ./MF2_pic9_head/train.lst and 0_noiselist.txt which has been generated in step 2. (We provide our denoised version(password: w9y5)

  2. Using the denoised head data (have removed the third type noise) and the tail data to continue the second stage training. It's noting that the training process need finetune manually by increase the --interweight gradually. When you change the interweight, you also need change the pretrained model by yourself, because we could not know which is the best model in the last training stage unless we test the model on the target dataset (MF2 test). We always finetune from the best model in the last training stage.

CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' python -u train_debug_soft_gs.py --network r50 --loss-type 4 --data-dir ./MF2_pic9_head_denoise/ --data-dir-interclass ./MF2_pic9_tail/ --end-epoch 100000 --lr 0.001 --interweight 1 --bag-size 3600 --batch-size1 360 --batchsize_id 360 --batch-size2 40  --pretrained /home/zhongyaoyao/insightface/models/NRA_r50pic9/model,xx --prefix ../models/model_all/model 2>&1|tee all_r50.log
CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' python -u train_debug_soft_gs.py --network r50 --loss-type 4 --data-dir ./MF2_pic9_head_denoise/ --data-dir-interclass ./MF2_pic9_tail/ --end-epoch 100000 --lr 0.001 --interweight 5 --bag-size 3600 --batch-size1 360 --batchsize_id 360 --batch-size2 40  --pretrained ../models/model_all/model,xx --prefix ../models/model_all/model_s2 2>&1|tee all_r50_s2.log
Owner
Zhong Yaoyao
PhD student in BUPT
Zhong Yaoyao
Interacting Two-Hand 3D Pose and Shape Reconstruction from Single Color Image (ICCV 2021)

Interacting Two-Hand 3D Pose and Shape Reconstruction from Single Color Image Interacting Two-Hand 3D Pose and Shape Reconstruction from Single Color

75 Dec 02, 2022
Code repository of the paper Neural circuit policies enabling auditable autonomy published in Nature Machine Intelligence

Neural Circuit Policies Enabling Auditable Autonomy Online access via SharedIt Neural Circuit Policies (NCPs) are designed sparse recurrent neural net

8 Jan 07, 2023
Aesara is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

Aesara is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

Aesara 898 Jan 07, 2023
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training

ActNN : Activation Compressed Training This is the official project repository for ActNN: Reducing Training Memory Footprint via 2-Bit Activation Comp

UC Berkeley RISE 178 Jan 05, 2023
ManipNet: Neural Manipulation Synthesis with a Hand-Object Spatial Representation - SIGGRAPH 2021

ManipNet: Neural Manipulation Synthesis with a Hand-Object Spatial Representation - SIGGRAPH 2021 Dataset Code Demos Authors: He Zhang, Yuting Ye, Tak

HE ZHANG 194 Dec 06, 2022
EMNLP'2021: SimCSE: Simple Contrastive Learning of Sentence Embeddings

SimCSE: Simple Contrastive Learning of Sentence Embeddings This repository contains the code and pre-trained models for our paper SimCSE: Simple Contr

Princeton Natural Language Processing 2.5k Dec 29, 2022
​ This is the Pytorch implementation of Progressive Attentional Manifold Alignment.

PAMA This is the Pytorch implementation of Progressive Attentional Manifold Alignment. Requirements python 3.6 pytorch 1.2.0+ PIL, numpy, matplotlib C

98 Nov 15, 2022
Implementation of Restricted Boltzmann Machine (RBM) and its variants in Tensorflow

xRBM Library Implementation of Restricted Boltzmann Machine (RBM) and its variants in Tensorflow Installation Using pip: pip install xrbm Examples Tut

Omid Alemi 55 Dec 29, 2022
List of all dependencies affected by node-ipc malicious commit

node-ipc-dependencies-list List of all dependencies affected by node-ipc malicious commit as of 17/3/2022 - 19/3/2022 (timestamp) Please improve upon

99 Oct 15, 2022
PyTorch code for the NAACL 2021 paper "Improving Generation and Evaluation of Visual Stories via Semantic Consistency"

Improving Generation and Evaluation of Visual Stories via Semantic Consistency PyTorch code for the NAACL 2021 paper "Improving Generation and Evaluat

Adyasha Maharana 28 Dec 08, 2022
Code for the paper "Next Generation Reservoir Computing"

Next Generation Reservoir Computing This is the code for the results and figures in our paper "Next Generation Reservoir Computing". They are written

OSU QuantInfo Lab 105 Dec 20, 2022
Keyword2Text This repository contains the code of the paper: "A Plug-and-Play Method for Controlled Text Generation"

Keyword2Text This repository contains the code of the paper: "A Plug-and-Play Method for Controlled Text Generation", if you find this useful and use

57 Dec 27, 2022
joint detection and semantic segmentation, based on ultralytics/yolov5,

Multi YOLO V5——Detection and Semantic Segmentation Overeview This is my undergraduate graduation project which based on ultralytics YOLO V5 tag v5.0.

477 Jan 06, 2023
BiSeNet based on pytorch

BiSeNet BiSeNet based on pytorch 0.4.1 and python 3.6 Dataset Download CamVid dataset from Google Drive or Baidu Yun(6xw4). Pretrained model Download

367 Dec 26, 2022
A PyTorch-based open-source framework that provides methods for improving the weakly annotated data and allows researchers to efficiently develop and compare their own methods.

Knodle (Knowledge-supervised Deep Learning Framework) - a new framework for weak supervision with neural networks. It provides a modularization for se

93 Nov 06, 2022
Supervised & unsupervised machine-learning techniques are applied to the database of weighted P4s which admit Calabi-Yau hypersurfaces.

Weighted Projective Spaces ML Description: The database of 5-vectors describing 4d weighted projective spaces which admit Calabi-Yau hypersurfaces are

Ed Hirst 3 Sep 08, 2022
An implementation for Neural Architecture Search with Random Labels (CVPR 2021 poster) on Pytorch.

Neural Architecture Search with Random Labels(RLNAS) Introduction This project provides an implementation for Neural Architecture Search with Random L

18 Nov 08, 2022
OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers (NeurIPS 2021)

OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers (NeurIPS 2021) This is an PyTorch implementation of OpenMatc

Vision and Learning Group 38 Dec 26, 2022
This project aims at providing a concise, easy-to-use, modifiable reference implementation for semantic segmentation models using PyTorch.

Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)

2.4k Jan 08, 2023
A clean and scalable template to kickstart your deep learning project 🚀 ⚡ 🔥

Lightning-Hydra-Template A clean and scalable template to kickstart your deep learning project 🚀 ⚡ 🔥 Click on Use this template to initialize new re

Hyunsoo Cho 1 Dec 20, 2021