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README.md
280
README.md
@@ -9,48 +9,39 @@ The code of InsightFace is released under the MIT License.
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## Recent Update
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**`2018.02.13`**: We achieved state-of-the-art performance on [MegaFace-Challenge](http://megaface.cs.washington.edu/results/facescrub.html). Please check our paper and code for implementation details.
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**`2018.02.13`**: We achieved state-of-the-art performance on [MegaFace-Challenge](http://megaface.cs.washington.edu/results/facescrub.html). Please check our paper and code for implementation details.
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## Contents
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[Deep Face Recognition](#deep-face-recognition)
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- [Introduction](#introduction)
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- [Training Data](#training-Data)
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- [Train](#train)
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- [Pretrained Models](#pretrained-models)
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- [Test on MegaFace](#test-on-megaface)
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- [Feature Embedding](#feature-embedding)
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- [Third-party Re-implementation](#third-party-re-implementation)
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[Face Alignment](#face-alignment)
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[Face Detection](#face-detection)
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[Citation](#citation)
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[Contact](#contact)
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[Deep Face Recognition](#deep-face-recognition)
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- [Introduction](#introduction)
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- [Training Data](#training-Data)
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- [Train](#train)
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- [Pretrained Models](#pretrained-models)
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- [Test on MegaFace](#test-on-megaface)
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- [Feature Embedding](#feature-embedding)
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- [Third-party Re-implementation](#third-party-re-implementation)
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[Face Alignment](#face-alignment)
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[Face Detection](#face-detection)
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[Citation](#citation)
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[Contact](#contact)
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## Deep Face Recognition
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### Introduction
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In this repository, we provide training data, network settings and loss designs for deep face recognition.
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In this repository, we provide training data, network settings and loss designs for deep face recognition.
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The training data includes the normalised MS1M and VGG2 datasets, which were already packed in the MxNet binary format.
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The network backbones include ResNet, InceptionResNet_v2, DenseNet, DPN and MobiletNet.
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The loss functions include Softmax, SphereFace, CosineFace, ArcFace and Triplet (Euclidean/Angular) Loss.
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* loss-type=0: Softmax
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* loss-type=1: SphereFace
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* loss-type=2: CosineFace
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* loss-type=4: ArcFace (Our Method)
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* loss-type=12: TripletLoss
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The training data includes the normalised MS1M and VGG2 datasets, which were already packed in the MxNet binary format.
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The network backbones include ResNet, InceptionResNet_v2, DenseNet, DPN and MobiletNet.
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The loss functions include Softmax, SphereFace, CosineFace, ArcFace and Triplet (Euclidean/Angular) Loss.
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* loss-type=0: Softmax
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* loss-type=1: SphereFace
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* loss-type=2: CosineFace
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* loss-type=4: ArcFace (Our Method)
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* loss-type=12: TripletLoss
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Our method, ArcFace, was initially described in an [arXiv technical report](https://arxiv.org/abs/1801.07698). By using this repository, you can simply achieve LFW 99.80%+ and Megaface 98%+ by a single model. This repository can help researcher/engineer to develop deep face recognition algorithms quickly by only two steps: download the binary dataset and run the training script.
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Our method, ArcFace, was initially described in an [arXiv technical report](https://arxiv.org/abs/1801.07698). By using this repository, you can simply achieve LFW 99.80%+ and Megaface 98%+ by a single model. This repository can help researcher/engineer to develop deep face recognition algorithms quickly by only two steps: download the binary dataset and run the training script.
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### Training Data
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@@ -60,185 +51,148 @@ All face images are aligned by MTCNN and cropped to 112x112:
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* [VGGFace2@BaiduDrive](https://pan.baidu.com/s/1c3KeLzy), [VGGFace2@GoogleDrive](https://drive.google.com/open?id=1KORwx_DWyIScAjD6vbo4CSRu048APoum)
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* Please check *src/data/face2rec2.py* on how to build a binary face dataset. Any public available MTCNN can be used to align the faces, and the performance should not change. We will improve the face normalisation step by full pose alignment methods recently.
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If you use the refined MS1M dataset we provided, please cite the original paper below:
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```
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@inproceedings{guo2016ms,
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title={Ms-celeb-1m: A dataset and benchmark for large-scale face recognition},
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author={Guo, Yandong and Zhang, Lei and Hu, Yuxiao and He, Xiaodong and Gao, Jianfeng},
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booktitle={European Conference on Computer Vision},
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pages={87--102},
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year={2016},
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organization={Springer}
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}
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```
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If you use the cropped version of VGG2 dataset we provided, please check its license [here](https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/licence.txt) and cite the original paper below:
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```
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@article{cao2017vggface2,
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title={VGGFace2: A dataset for recognising faces across pose and age},
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author={Cao, Qiong and Shen, Li and Xie, Weidi and Parkhi, Omkar M and Zisserman, Andrew},
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journal={arXiv:1710.08092},
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year={2017}
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}
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```
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**Note:** If you use the refined [MS1M](https://arxiv.org/abs/1607.08221) dataset and the cropped [VGG2](https://arxiv.org/abs/1710.08092) dataset, please cite the original papers.
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### Train
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1. Install `MXNet` with GPU support (Python 2.7).
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```
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pip install mxnet-cu80
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```
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```
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pip install mxnet-cu80
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```
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2. Clone the InsightFace repository. We call the directory insightface as **`INSIGHTFACE_ROOT`**.
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```
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git clone --recursive https://github.com/deepinsight/insightface.git
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```
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```
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git clone --recursive https://github.com/deepinsight/insightface.git
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```
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3. Download the training set (`MS1M`) and place it in **`$INSIGHTFACE_ROOT/datasets/`**. Each training dataset includes following 7 files:
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```Shell
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faces_ms1m_112x112/
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train.idx
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train.rec
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property
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lfw.bin
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cfp_ff.bin
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cfp_fp.bin
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agedb_30.bin
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```
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```Shell
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faces_ms1m_112x112/
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train.idx
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train.rec
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property
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lfw.bin
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cfp_ff.bin
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cfp_fp.bin
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agedb_30.bin
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```
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The first three files are the training dataset while the last four files are verification sets.
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The first three files are the training dataset while the last four files are verification sets.
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4. Train deep face recognition models.
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**Note:** In this part, we assume you are in the directory **`$INSIGHTFACE_ROOT/src/`**.
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```
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export MXNET_CPU_WORKER_NTHREADS=24
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export MXNET_ENGINE_TYPE=ThreadedEnginePerDevice
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```
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We give some examples below. Our experiments were conducted on the Tesla P40 GPU.
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In this part, we assume you are in the directory **`$INSIGHTFACE_ROOT/src/`**.
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```
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export MXNET_CPU_WORKER_NTHREADS=24
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export MXNET_ENGINE_TYPE=ThreadedEnginePerDevice
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```
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We give some examples below. Our experiments were conducted on the Tesla P40 GPU.
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(1). Train ArcFace with LResNet100E-IR.
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```Shell
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CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train_softmax.py --network r100 --loss-type 4 --margin-m 0.5 --data-dir ../datasets/faces_ms1m_112x112 --prefix ../model-r100
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```
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It will output verification results of *LFW*, *CFP-FF*, *CFP-FP* and *AgeDB-30* every 2000 batches. You can check all command line options in **train\_softmax.py**.
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This model can achieve **LFW 99.80+ and MegaFace 98.0%+**
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```Shell
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CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train_softmax.py --network r100 --loss-type 4 --margin-m 0.5 --data-dir ../datasets/faces_ms1m_112x112 --prefix ../model-r100
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```
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It will output verification results of *LFW*, *CFP-FF*, *CFP-FP* and *AgeDB-30* every 2000 batches. You can check all command line options in **train\_softmax.py**.
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This model can achieve **LFW 99.80+ and MegaFace 98.0%+**
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(2). Train CosineFace with LResNet50E-IR.
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```Shell
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CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train_softmax.py --network r50 --loss-type 2 --margin-m 0.35 --data-dir ../datasets/faces_ms1m_112x112 --prefix ../model-r50-amsoftmax
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```
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```Shell
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CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train_softmax.py --network r50 --loss-type 2 --margin-m 0.35 --data-dir ../datasets/faces_ms1m_112x112 --prefix ../model-r50-amsoftmax
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```
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(3). Train Softmax with LMobileNetE.
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```Shell
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CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train_softmax.py --network m1 --loss-type 0 --data-dir ../datasets/faces_ms1m_112x112 --prefix ../model-m1-softmax
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```
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```Shell
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CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train_softmax.py --network m1 --loss-type 0 --data-dir ../datasets/faces_ms1m_112x112 --prefix ../model-m1-softmax
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```
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(4). Fine-turn the above Softmax model with Triplet loss.
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```Shell
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CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train_softmax.py --network m1 --loss-type 12 --lr 0.005 --mom 0.0 --per-batch-size 150 --data-dir ../datasets/faces_ms1m_112x112 --pretrained ../model-m1-softmax,50 --prefix ../model-m1-triplet
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```
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```Shell
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CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train_softmax.py --network m1 --loss-type 12 --lr 0.005 --mom 0.0 --per-batch-size 150 --data-dir ../datasets/faces_ms1m_112x112 --pretrained ../model-m1-softmax,50 --prefix ../model-m1-triplet
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```
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(5). Train LDPN107E network with Softmax loss on VGGFace2 dataset.
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```Shell
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CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' python -u train_softmax.py --network p107 --loss-type 0 --per-batch-size 64 --data-dir ../datasets/faces_vgg_112x112 --prefix ../model-p107-softmax
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```
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```Shell
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CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' python -u train_softmax.py --network p107 --loss-type 0 --per-batch-size 64 --data-dir ../datasets/faces_vgg_112x112 --prefix ../model-p107-softmax
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```
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5. Verification results.
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**LResNet100E-IR** network trained on **MS1M** dataset with ArcFace loss:
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**LResNet100E-IR** network trained on **MS1M** dataset with ArcFace loss:
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| Method | LFW(%) | CFP-FF(%) | CFP-FP(%) | AgeDB-30(%) |
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| ------- | ------ | --------- | --------- | ----------- |
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| Ours | 99.80+ | 99.85+ | 94.0+ | 97.90+ |
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| Method | LFW(%) | CFP-FF(%) | CFP-FP(%) | AgeDB-30(%) |
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| ------- | ------ | --------- | --------- | ----------- |
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| Ours | 99.80+ | 99.85+ | 94.0+ | 97.90+ |
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**LResNet50E-IR** network trained on **VGGFace2** dataset with ArcFace loss:
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**LResNet50E-IR** network trained on **VGGFace2** dataset with ArcFace loss:
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| Method | LFW(%) | CFP-FF(%) | CFP-FP(%) | AgeDB-30(%) |
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| ------- | ------ | --------- | --------- | ----------- |
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| Ours | 99.7+ | 99.6+ | 97.1+ | 95.7+ |
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| Method | LFW(%) | CFP-FF(%) | CFP-FP(%) | AgeDB-30(%) |
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| ------- | ------ | --------- | --------- | ----------- |
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| Ours | 99.7+ | 99.6+ | 97.1+ | 95.7+ |
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We report the verification accuracy after removing training set overlaps to strictly follow the evaluation metric. `(C) means after cleaning`
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We report the verification accuracy after removing training set overlaps to strictly follow the evaluation metric. `(C) means after cleaning`
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| Dataset | Identities | Images | Identites(C) | Images(C) | Acc | Acc(C) |
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| -------- | ---------- | ------- | ------------ | --------- | ----- | ------ |
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| LFW | 85742 | 3850179 | 80995 | 3586128 | 99.83 | 99.81 |
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| CFP-FP | 85742 | 3850179 | 83706 | 3736338 | 94.04 | 94.03 |
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| AgeDB-30 | 85742 | 3850179 | 83775 | 3761329 | 98.08 | 97.87 |
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| Dataset | Identities | Images | Identites(C) | Images(C) | Acc | Acc(C) |
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| -------- | ---------- | ------- | ------------ | --------- | ----- | ------ |
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| LFW | 85742 | 3850179 | 80995 | 3586128 | 99.83 | 99.81 |
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| CFP-FP | 85742 | 3850179 | 83706 | 3736338 | 94.04 | 94.03 |
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| AgeDB-30 | 85742 | 3850179 | 83775 | 3761329 | 98.08 | 97.87 |
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### Pretrained Models
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1. [LResNet50E-IR@BaiduDrive](https://pan.baidu.com/s/1mj6X7MK), [@GoogleDrive](https://drive.google.com/open?id=1x0-EiYX9jMUKiq-n1Bd9OCK4fVB3a54v)
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You can use `$INSIGHTFACE/src/eval/verification.py` to test all the pre-trained models.
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Performance:
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1. [LResNet50E-IR@BaiduDrive](https://pan.baidu.com/s/1mj6X7MK), [@GoogleDrive](https://drive.google.com/open?id=1x0-EiYX9jMUKiq-n1Bd9OCK4fVB3a54v)
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Performance:
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| Method | LFW(%) | CFP-FF(%) | CFP-FP(%) | AgeDB-30(%) | MegaFace(%) |
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| ------- | ------ | --------- | --------- | ----------- | ------------- |
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| Ours | 99.80 | 99.83 | 92.74 | 97.76 | 97.64 |
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| Method | LFW(%) | CFP-FF(%) | CFP-FP(%) | AgeDB-30(%) | MegaFace(%) |
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| ------- | ------ | --------- | --------- | ----------- | ------------- |
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| Ours | 99.80 | 99.83 | 92.74 | 97.76 | 97.64 |
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You can use `$INSIGHTFACE/src/eval/verification.py` to test all the pre-trained models.
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2. [LResNet34E-IR@BaiduDrive](https://pan.baidu.com/s/1jKahEXw)
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Performance:
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2. [LResNet34E-IR@BaiduDrive](https://pan.baidu.com/s/1jKahEXw)
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| Method | LFW(%) | CFP-FF(%) | CFP-FP(%) | AgeDB-30(%) | MegaFace(%) |
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| ------- | ------ | --------- | --------- | ----------- | ------------- |
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| Ours | 99.65 | 99.77 | 92.12 | 97.70 | 96.70 |
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Performance:
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**`Caffe`** [LResNet34E-IR@BaiduDrive](https://pan.baidu.com/s/1bpRsvYR), converted by the above MXNet model.
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Performance:
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| Method | LFW(%) | CFP-FF(%) | CFP-FP(%) | AgeDB-30(%) | MegaFace(%) |
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| ------- | ------ | --------- | --------- | ----------- | ------------- |
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| Ours | 99.65 | 99.77 | 92.12 | 97.70 | 96.70 |
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**`Caffe`** [LResNet34E-IR@BaiduDrive](https://pan.baidu.com/s/1bpRsvYR), converted by the above MXNet model.
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Performance:
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| Method | LFW(%) | CFP-FF(%) | CFP-FP(%) | AgeDB-30(%) | MegaFace1M(%) |
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| ------- | ------ | --------- | --------- | ----------- | ------------- |
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| Ours | 99.46 | 99.60 | 87.75 | 96.00 | 93.29 |
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| Method | LFW(%) | CFP-FF(%) | CFP-FP(%) | AgeDB-30(%) | MegaFace1M(%) |
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| ------- | ------ | --------- | --------- | ----------- | ------------- |
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| Ours | 99.46 | 99.60 | 87.75 | 96.00 | 93.29 |
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### Test on MegaFace
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**Note:** In this part, we assume you are in the directory **`$INSIGHTFACE_ROOT/src/megaface/`**
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We found there are overlap identities between facescrub dataset and Megaface distractors, which significantly affects the identification performance. This list is released under **`$INSIGHTFACE_ROOT/src/megaface/`**.
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1. Align all face images of facescrub dataset and megaface distractors. Please check the alignment scripts under **`$INSIGHTFACE_ROOT/src/align/`**.
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2. Generate feature files for both facescrub and megaface images.
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```
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python -u gen_megaface.py
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```
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3. Remove Megaface noises which generates new feature files.
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```
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python -u remove_noises.py
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```
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4. Run megaface development kit to produce final result.
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In this part, we assume you are in the directory **`$INSIGHTFACE_ROOT/src/megaface/`**
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**Note:** We found there are overlap identities between facescrub dataset and Megaface distractors, which significantly affects the identification performance. This list is released under **`$INSIGHTFACE_ROOT/src/megaface/`**.
|
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1. Align all face images of facescrub dataset and megaface distractors. Please check the alignment scripts under **`$INSIGHTFACE_ROOT/src/align/`**.
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2. Generate feature files for both facescrub and megaface images.
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```
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python -u gen_megaface.py
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```
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3. Remove Megaface noises which generates new feature files.
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```
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python -u remove_noises.py
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```
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4. Run megaface development kit to produce final result.
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### Feature Embedding
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**Note:** In this part, we assume you are in the directory **`$INSIGHTFACE_ROOT/deploy/`**.
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In this part, we assume you are in the directory **`$INSIGHTFACE_ROOT/deploy/`**. The input face image should be generally centre cropped. We use *(RNet+)ONet* of *MTCNN* to further align the image before sending it to the feature embedding network.
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1. Prepare a pre-trained model.
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1. Prepare a pre-trained model.
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2. Put the model under **`$INSIGHTFACE_ROOT/models/`**. For example, **`$INSIGHTFACE_ROOT/models/model-r34-amf`**.
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3. Run the test script **`$INSIGHTFACE_ROOT/deploy/test.py`**.
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2. Put the model under **`$INSIGHTFACE_ROOT/models/`**. For example, **`$INSIGHTFACE_ROOT/models/model-r34-amf/`**.
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3. Run the test script **`$INSIGHTFACE_ROOT/deploy/test.py`**.
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Note that we do not require the input face image to be aligned but it should be general centre cropped. We use *(RNet+)ONet* of *MTCNN* to further align the image before sending it to the feature embedding network.
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For single cropped face image(112x112), total inference time is only 17ms on my testing server(Intel E5-2660 @ 2.00GHz, Tesla M40, *LResNet34E-IR*).
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For single cropped face image(112x112), total inference time is only 17ms on our testing server(Intel E5-2660 @ 2.00GHz, Tesla M40, *LResNet34E-IR*).
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||||
### Third-party Re-implementation
|
||||
|
||||
@@ -254,14 +208,14 @@ Todo
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||||
|
||||
## Citation
|
||||
|
||||
If you find **InsightFace** useful in your research, please consider to cite the following papers:
|
||||
If you find **InsightFace** useful in your research, please consider to cite the following related papers:
|
||||
|
||||
```
|
||||
@article{deng2018arcface,
|
||||
title={ArcFace: Additive Angular Margin Loss for Deep Face Recognition},
|
||||
author={Deng, Jiankang and Guo, Jia and Zafeiriou, Stefanos},
|
||||
journal={arXiv:1801.07698},
|
||||
year={2018}
|
||||
title={ArcFace: Additive Angular Margin Loss for Deep Face Recognition},
|
||||
author={Deng, Jiankang and Guo, Jia and Zafeiriou, Stefanos},
|
||||
journal={arXiv:1801.07698},
|
||||
year={2018}
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
Reference in New Issue
Block a user