From a307696b5587fa9bf6c00347e4a2f7bb5b3199e3 Mon Sep 17 00:00:00 2001 From: "mingjiang.li" Date: Tue, 25 Feb 2025 15:11:01 +0800 Subject: [PATCH 1/6] move face/arcface model to face_recognition/ Signed-off-by: mingjiang.li --- .../arcface/pytorch/.gitignore | 0 .../arcface/pytorch/LICENSE | 0 .../arcface/pytorch/README.md | 0 .../arcface/pytorch/arcface.py | 0 .../arcface/pytorch/eval_LFW.py | 0 .../arcface/pytorch/img/1_001.jpg | Bin .../arcface/pytorch/img/1_002.jpg | Bin .../arcface/pytorch/img/2_001.jpg | Bin .../arcface/pytorch/nets/__init__.py | 0 .../arcface/pytorch/nets/arcface.py | 0 .../arcface/pytorch/nets/arcface_training.py | 0 .../arcface/pytorch/nets/iresnet.py | 0 .../arcface/pytorch/nets/mobilefacenet.py | 0 .../arcface/pytorch/nets/mobilenet.py | 0 .../arcface/pytorch/predict.py | 0 .../arcface/pytorch/requirements.txt | 0 .../arcface/pytorch/run.sh | 0 .../arcface/pytorch/summary.py | 0 .../arcface/pytorch/train.py | 0 .../arcface/pytorch/txt_annotation.py | 0 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to cv/face_recognition/arcface/pytorch/utils/utils.py diff --git a/cv/face/arcface/pytorch/utils/utils_fit.py b/cv/face_recognition/arcface/pytorch/utils/utils_fit.py similarity index 100% rename from cv/face/arcface/pytorch/utils/utils_fit.py rename to cv/face_recognition/arcface/pytorch/utils/utils_fit.py diff --git a/cv/face/arcface/pytorch/utils/utils_metrics.py b/cv/face_recognition/arcface/pytorch/utils/utils_metrics.py similarity index 100% rename from cv/face/arcface/pytorch/utils/utils_metrics.py rename to cv/face_recognition/arcface/pytorch/utils/utils_metrics.py -- Gitee From 3f6ba12cd8ad3203e397a9232448b43e921c05b3 Mon Sep 17 00:00:00 2001 From: "mingjiang.li" Date: Tue, 25 Feb 2025 15:12:01 +0800 Subject: [PATCH 2/6] move face/blazeface/ model to face_recognition/ Signed-off-by: mingjiang.li --- cv/{face => face_recognition}/blazeface/paddlepaddle/README.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename cv/{face => 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b/cv/face_recognition/facenet/tensorflow/train_facenet_ddp.sh similarity index 100% rename from cv/face/facenet/tensorflow/train_facenet_ddp.sh rename to cv/face_recognition/facenet/tensorflow/train_facenet_ddp.sh diff --git a/cv/face/facenet/tensorflow/util/plot_learning_curves.m b/cv/face_recognition/facenet/tensorflow/util/plot_learning_curves.m similarity index 100% rename from cv/face/facenet/tensorflow/util/plot_learning_curves.m rename to cv/face_recognition/facenet/tensorflow/util/plot_learning_curves.m -- Gitee From 3a11f8f453cb5582a0acb89565947bed249bd16e Mon Sep 17 00:00:00 2001 From: "mingjiang.li" Date: Tue, 25 Feb 2025 15:20:47 +0800 Subject: [PATCH 5/6] format face recognition models readme Signed-off-by: mingjiang.li --- README.md | 20 +++++------ cv/face_recognition/arcface/pytorch/README.md | 12 ++++--- .../blazeface/paddlepaddle/README.md | 14 ++++++-- cv/face_recognition/cosface/pytorch/README.md | 13 +++---- cv/face_recognition/facenet/pytorch/README.md | 18 +++++----- .../facenet/tensorflow/README.md | 34 ++++++++++++------- 6 files changed, 68 insertions(+), 43 deletions(-) diff --git a/README.md b/README.md index f67e77f4..8125e741 100644 --- a/README.md +++ b/README.md @@ -107,19 +107,19 @@ DeepSparkHub甄选上百个应用算法和模型，覆盖AI和通用计算各领 #### Face Detection -| Model | Framework | Dataset | -|------------------------------------------|-----------|------------| -| [RetinaFace](cv/face/retinaface/pytorch) | PyTorch | WIDER FACE | +| Model | Framework | Dataset | +|----------------------------------------------------|-----------|------------| +| [RetinaFace](cv/face_detection/retinaface/pytorch) | PyTorch | WIDER FACE | #### Face Recognition -| Model | Framework | Dataset | -|---------------------------------------------|--------------|--------------------| -| [ArcFace](cv/face/arcface/pytorch) | PyTorch | CASIA-WebFaces&LFW | -| [BlazeFace](cv/face/blazeface/paddlepaddle) | PaddlePaddle | WIDER FACE | -| [CosFace](cv/face/cosface/pytorch) | PyTorch | CASIA-WebFaces&LFW | -| [FaceNet](cv/face/facenet/pytorch) | PyTorch | CASIA-WebFaces&LFW | -| [FaceNet](cv/face/facenet/tensorflow) | TensorFlow | CASIA-WebFaces&LFW | +| Model | Framework | Dataset | +|---------------------------------------------------------|--------------|--------------------| +| [ArcFace](cv/face_recognition//arcface/pytorch) | PyTorch | CASIA-WebFaces&LFW | +| [BlazeFace](cv/face_recognition/blazeface/paddlepaddle) | PaddlePaddle | WIDER FACE | +| [CosFace](cv/face_recognition/cosface/pytorch) | PyTorch | CASIA-WebFaces&LFW | +| [FaceNet](cv/face_recognition/facenet/pytorch) | PyTorch | CASIA-WebFaces&LFW | +| [FaceNet](cv/face_recognition/facenet/tensorflow) | TensorFlow | CASIA-WebFaces&LFW | #### Instance Segmentation diff --git a/cv/face_recognition/arcface/pytorch/README.md b/cv/face_recognition/arcface/pytorch/README.md index 0ddc780f..f478e2e6 100644 --- a/cv/face_recognition/arcface/pytorch/README.md +++ b/cv/face_recognition/arcface/pytorch/README.md @@ -1,7 +1,11 @@ # ArcFace ## Model description -This repo is a pytorch implement of ArcFace, which propose an Additive Angular Margin Loss to obtain highly discriminative features for face recognition. The proposed ArcFace has a clear geometric interpretation due to the exact correspondence to the geodesic distance on the hypersphere. ArcFace consistently outperforms the state-of-the-art and can be easily implemented with negligible computational overhead + +This repo is a pytorch implement of ArcFace, which propose an Additive Angular Margin Loss to obtain highly +discriminative features for face recognition. The proposed ArcFace has a clear geometric interpretation due to the exact +correspondence to the geodesic distance on the hypersphere. ArcFace consistently outperforms the state-of-the-art and +can be easily implemented with negligible computational overhead ## Step 1: Installation @@ -36,10 +40,10 @@ bash run.sh $GPUS ## Results -| model | FPS | LFW_Accuracy | +| Model | FPS | LFW_Accuracy | |---------|--------| -----------------| -| arcface | 38.272 | 0.99000+-0.00615 | +| ArcFace | 38.272 | 0.99000+-0.00615 | ## Reference -- [arcface-pytorch](https://github.com/bubbliiiing/arcface-pytorch) +- [arcface-pytorch](https://github.com/bubbliiiing/arcface-pytorch) diff --git a/cv/face_recognition/blazeface/paddlepaddle/README.md b/cv/face_recognition/blazeface/paddlepaddle/README.md index e71aafd0..b5b510c8 100644 --- a/cv/face_recognition/blazeface/paddlepaddle/README.md +++ b/cv/face_recognition/blazeface/paddlepaddle/README.md @@ -1,9 +1,12 @@ # BlazeFace ## Model description -BlazeFace is Google Research published face detection model. It's lightweight but good performance, and tailored for mobile GPU inference. It runs at a speed of 200-1000+ FPS on flagship devices. + +BlazeFace is Google Research published face detection model. It's lightweight but good performance, and tailored for +mobile GPU inference. It runs at a speed of 200-1000+ FPS on flagship devices. ## Step 1: Installing + ```bash git clone https://github.com/PaddlePaddle/PaddleDetection.git ``` @@ -20,11 +23,13 @@ pip3 install install numba==0.56.4 ``` ## Step 2: Prepare Datasets + We use WIDER-FACE dataset for training and model tests, the official web site provides detailed data is introduced. - WIDER-Face data source: - Load a dataset of type wider_face using the following directory structure: + ```bash dataset/wider_face/ ├── wider_face_split @@ -47,7 +52,9 @@ dataset/wider_face/ │ │ ├── 10--People_Marching │ │ │ ... ``` + - Manually download the dataset: To download the WIDER-FACE dataset, run the following command: + ```bash cd dataset/wider_face && ./download_wider_face.sh ``` @@ -61,7 +68,9 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/face_detection/blazeface_fpn_ssh_1000e.yml ``` + ## Step 4: Evaluation + ```bash python3 -u tools/eval.py -c configs/face_detection/blazeface_fpn_ssh_1000e.yml \ -o weights=output/blazeface_fpn_ssh_1000e/model_final.pdopt \ @@ -76,11 +85,12 @@ python3 setup.py build_ext --inplace python3 evaluation.py -p ../output/pred/ -g ../eval_tools/ground_truth ``` - ## Step 5: result | GPUs | Network structure | Easy/Medium/Hard Set | Ips |-------------|-----------|-----------|---—------| | BI-V100 x 8 | BlazeFace-FPN-SSH | 0.886/0.860/0.753 | 8.6813 | + ## Reference + - [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) diff --git a/cv/face_recognition/cosface/pytorch/README.md b/cv/face_recognition/cosface/pytorch/README.md index caea2d80..e2076e8e 100644 --- a/cv/face_recognition/cosface/pytorch/README.md +++ b/cv/face_recognition/cosface/pytorch/README.md @@ -2,7 +2,9 @@ ## Model description -CosFace is a face recognition model that achieves state-of-the-art results by introducing a cosine margin penalty in the loss function when training the neural network, which learns highly discriminative facial embeddings by maximizing inter-class differences and minimizing intra-class variations. +CosFace is a face recognition model that achieves state-of-the-art results by introducing a cosine margin penalty in the +loss function when training the neural network, which learns highly discriminative facial embeddings by maximizing +inter-class differences and minimizing intra-class variations. ## Step 1: Installation @@ -41,11 +43,10 @@ bash train.sh 0,1,2,3,4,5,6,7 ## Results -| model | FPS | LFW_Accuracy | -|---------|--------| -----------------| -| Cosface | 5492.08 | 0.9865 | +| Model | FPS | LFW_Accuracy | +|---------|---------|--------------| +| Cosface | 5492.08 | 0.9865 | ## Reference -- [CosFace_pytorch](https://github.com/MuggleWang/CosFace_pytorch) - +- [CosFace_pytorch](https://github.com/MuggleWang/CosFace_pytorch) diff --git a/cv/face_recognition/facenet/pytorch/README.md b/cv/face_recognition/facenet/pytorch/README.md index 53f10d95..3cd9d1f5 100644 --- a/cv/face_recognition/facenet/pytorch/README.md +++ b/cv/face_recognition/facenet/pytorch/README.md @@ -3,13 +3,12 @@ ## Model Description This is a facenet-pytorch library that can be used to train your own face recognition model. -> Despite significant recent advances in the field of face recognition, implementing face -verification and recognition efficiently at scale presents serious challenges to current -approaches. In this paper we present a system, called FaceNet, that directly learns a mapping -from face images to a compact Euclidean space where distances directly correspond to a measure -of face similarity. Once this space has been produced, tasks such as face recognition, -verification and clustering can be easily implemented using standard techniques with FaceNet -embeddings as feature vectors. +> Despite significant recent advances in the field of face recognition, implementing face verification and recognition +efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called +FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly +correspond to a measure of face similarity. Once this space has been produced, tasks such as face recognition, +verification and clustering can be easily implemented using standard techniques with FaceNet embeddings as feature +vectors. ## Step 1: Installation @@ -49,10 +48,11 @@ python3 train.py ## Results -| model | FPS | LFW_Accuracy | +| Model | FPS | LFW_Accuracy | |---------|--------| -----------------| -| facenet | 1256.96| 0.97933+-0.00624 | +| Facenet | 1256.96| 0.97933+-0.00624 | ## Reference + - [paper](https://arxiv.org/abs/1503.03832) - [facenet-pytorch](https://github.com/bubbliiiing/facenet-pytorch) diff --git a/cv/face_recognition/facenet/tensorflow/README.md b/cv/face_recognition/facenet/tensorflow/README.md index e4f5cd5a..547d4e76 100644 --- a/cv/face_recognition/facenet/tensorflow/README.md +++ b/cv/face_recognition/facenet/tensorflow/README.md @@ -1,4 +1,4 @@ -## Facenet +# Facenet ## Model description @@ -14,8 +14,11 @@ pip3 install numpy==1.23.5 ## Step 2: Preparing datasets -The [CASIA-WebFace](http://www.cbsr.ia.ac.cn/english/CASIA-WebFace-Database.html) dataset has been used for training. This training set consists of total of 453 453 images over 10 575 identities after face detection. Some performance improvement has been seen if the dataset has been filtered before training. Some more information about how this was done will come later. -The best performing model has been trained on the [VGGFace2](https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/) dataset consisting of ~3.3M faces and ~9000 classes. +The [CASIA-WebFace](http://www.cbsr.ia.ac.cn/english/CASIA-WebFace-Database.html) dataset has been used for training. +This training set consists of total of 453 453 images over 10 575 identities after face detection. Some performance +improvement has been seen if the dataset has been filtered before training. Some more information about how this was +done will come later. The best performing model has been trained on the +[VGGFace2](https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/) dataset consisting of ~3.3M faces and ~9000 classes. ## download @@ -38,13 +41,21 @@ lfw lfw_160 lfw.tgz ### Face alignment using MTCNN -One problem with the above approach seems to be that the Dlib face detector misses some of the hard examples (partial occlusion, silhouettes, etc). This makes the training set too "easy" which causes the model to perform worse on other benchmarks. -To solve this, other face landmark detectors has been tested. One face landmark detector that has proven to work very well in this setting is the -[Multi-task CNN](https://kpzhang93.github.io/MTCNN_face_detection_alignment/index.html). A Matlab/Caffe implementation can be found [here](https://github.com/kpzhang93/MTCNN_face_detection_alignment) and this has been used for face alignment with very good results. A Python/Tensorflow implementation of MTCNN can be found [here](https://github.com/davidsandberg/facenet/tree/master/src/align). This implementation does not give identical results to the Matlab/Caffe implementation but the performance is very similar. +One problem with the above approach seems to be that the Dlib face detector misses some of the hard examples (partial +occlusion, silhouettes, etc). This makes the training set too "easy" which causes the model to perform worse on other +benchmarks. To solve this, other face landmark detectors has been tested. One face landmark detector that has proven to +work very well in this setting is the [Multi-task +CNN](https://kpzhang93.github.io/MTCNN_face_detection_alignment/index.html). A Matlab/Caffe implementation can be found +[here](https://github.com/kpzhang93/MTCNN_face_detection_alignment) and this has been used for face alignment with very +good results. A Python/Tensorflow implementation of MTCNN can be found +[here](https://github.com/davidsandberg/facenet/tree/master/src/align). This implementation does not give identical +results to the Matlab/Caffe implementation but the performance is very similar. ## Step 3: Training -Currently, the best results are achieved by training the model using softmax loss. Details on how to train a model using softmax loss on the CASIA-WebFace dataset can be found on the page [Classifier training of Inception-ResNet-v1](https://github.com/davidsandberg/facenet/wiki/Classifier-training-of-inception-resnet-v1) and . +Currently, the best results are achieved by training the model using softmax loss. Details on how to train a model using +softmax loss on the CASIA-WebFace dataset can be found on the page [Classifier training of +Inception-ResNet-v1](https://github.com/davidsandberg/facenet/wiki/Classifier-training-of-inception-resnet-v1) and . ### One Card @@ -61,11 +72,10 @@ bash train_facenet_ddp.sh ## Results - -| model | FPS | LFW_Accuracy | -| --------- | -------- | ------------------ | -| facenet | 216.96 | 0.98900+-0.00642 | +| Model | FPS | LFW_Accuracy | +|---------|--------|------------------| +| Facenet | 216.96 | 0.98900+-0.00642 | ## Reference -https://github.com/davidsandberg/facenet +- [facenet](https://github.com/davidsandberg/facenet) -- Gitee From 5cea5dbadcc3173a7edb981537f5c79d98b2945f Mon Sep 17 00:00:00 2001 From: "mingjiang.li" Date: Tue, 25 Feb 2025 15:22:41 +0800 Subject: [PATCH 6/6] clean face dir Signed-off-by: mingjiang.li --- README.md | 2 +- cv/face/README.md | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) delete mode 100644 cv/face/README.md diff --git a/README.md b/README.md index 8125e741..c9ff1c26 100644 --- a/README.md +++ b/README.md @@ -115,7 +115,7 @@ DeepSparkHub甄选上百个应用算法和模型，覆盖AI和通用计算各领 | Model | Framework | Dataset | |---------------------------------------------------------|--------------|--------------------| -| [ArcFace](cv/face_recognition//arcface/pytorch) | PyTorch | CASIA-WebFaces&LFW | +| [ArcFace](cv/face_recognition/arcface/pytorch) | PyTorch | CASIA-WebFaces&LFW | | [BlazeFace](cv/face_recognition/blazeface/paddlepaddle) | PaddlePaddle | WIDER FACE | | [CosFace](cv/face_recognition/cosface/pytorch) | PyTorch | CASIA-WebFaces&LFW | | [FaceNet](cv/face_recognition/facenet/pytorch) | PyTorch | CASIA-WebFaces&LFW | diff --git a/cv/face/README.md b/cv/face/README.md deleted file mode 100644 index a83efc05..00000000 --- a/cv/face/README.md +++ /dev/null @@ -1 +0,0 @@ -# Face Recognition -- Gitee