这是一个facenet-pytorch的库，可以用于训练自己的人脸识别模型。

Related tags

Deep Learningfacenet-pytorch
Overview

Facenet：人脸识别模型在Pytorch当中的实现

目录

  1. 性能情况 Performance
  2. 所需环境 Environment
  3. 注意事项 Attention
  4. 文件下载 Download
  5. 预测步骤 How2predict
  6. 训练步骤 How2train
  7. 参考资料 Reference

性能情况

训练数据集 权值文件名称 测试数据集 输入图片大小 accuracy
CASIA-WebFace facenet_mobilenet.pth LFW 160x160 98.23%
CASIA-WebFace facenet_inception_resnetv1.pth LFW 160x160 98.78%

所需环境

pytorch==1.2.0

文件下载

已经训练好的facenet_mobilenet.pth和facenet_inception_resnetv1.pth可以在百度网盘下载。
链接: https://pan.baidu.com/s/1slUYdpskFpUX62WpJeLByA 提取码: fe1w

训练用的CASIA-WebFaces数据集以及评估用的LFW数据集可以在百度网盘下载。
链接: https://pan.baidu.com/s/1fhiHlylAFVoR43yfDbi4Ag 提取码: gkch

预测步骤

a、使用预训练权重

  1. 下载完库后解压，在model_data文件夹里已经有了facenet_mobilenet.pth，可直接运行predict.py输入：
img\1_001.jpg
img\1_002.jpg
  1. 也可以在百度网盘下载facenet_inception_resnetv1.pth，放入model_data，修改facenet.py文件的model_path后，输入：
img\1_001.jpg
img\1_002.jpg

b、使用自己训练的权重

  1. 按照训练步骤训练。
  2. 在facenet.py文件里面，在如下部分修改model_path和backbone使其对应训练好的文件；model_path对应logs文件夹下面的权值文件，backbone对应主干特征提取网络。
_defaults = {
    "model_path"    : "model_data/facenet_mobilenet.pth",
    "input_shape"   : (160, 160, 3),
    "backbone"      : "mobilenet",
    "cuda"          : True,
}
  1. 运行predict.py，输入
img\1_001.jpg
img\1_002.jpg

训练步骤

  1. 本文使用如下格式进行训练。
|-datasets
    |-people0
        |-123.jpg
        |-234.jpg
    |-people1
        |-345.jpg
        |-456.jpg
    |-...
  1. 下载好数据集，将训练用的CASIA-WebFaces数据集以及评估用的LFW数据集，解压后放在根目录。
  2. 在训练前利用txt_annotation.py文件生成对应的cls_train.txt。
  3. 利用train.py训练facenet模型，训练前，根据自己的需要选择backbone，model_path和backbone一定要对应。
  4. 运行train.py即可开始训练。

评估步骤

  1. 下载好评估数据集，将评估用的LFW数据集，解压后放在根目录
  2. 在eval_LFW.py设置使用的主干特征提取网络和网络权值。
  3. 运行eval_LFW.py来进行模型准确率评估。

Reference

https://github.com/davidsandberg/facenet
https://github.com/timesler/facenet-pytorch

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Comments
  • 训练过程经常遇到BrokenPipeError: [Errno 32] Broken pipe

    训练过程经常遇到BrokenPipeError: [Errno 32] Broken pipe

    Epoch 1/100: 100%|██████████| 583/583 [07:36<00:00, 1.07it/s, accuracy=0.89, lr=0.01, total_CE_loss=9.02, total_triple_loss=0.101]Traceback (most recent call last): File "/opt/vitis_ai/conda/envs/vitis-ai-optimizer_pytorch/lib/python3.7/multiprocessing/queues.py", line 242, in _feed send_bytes(obj) File "/opt/vitis_ai/conda/envs/vitis-ai-optimizer_pytorch/lib/python3.7/multiprocessing/connection.py", line 200, in send_bytes self._send_bytes(m[offset:offset + size]) File "/opt/vitis_ai/conda/envs/vitis-ai-optimizer_pytorch/lib/python3.7/multiprocessing/connection.py", line 404, in _send_bytes self._send(header + buf) File "/opt/vitis_ai/conda/envs/vitis-ai-optimizer_pytorch/lib/python3.7/multiprocessing/connection.py", line 368, in _send n = write(self._handle, buf) BrokenPipeError: [Errno 32] Broken pipe

    opened by jia0511 1
  • lfw数据集处理有什么区别？ 精度目前97%

    lfw数据集处理有什么区别？ 精度目前97%

    使用facenet_mobilenet.pth 在 LFW 数据集上，调整图片大小为 160x160 ，得到了0.97的精度，没有到| 98.23%，而在百度网盘提供的slfw数据上，精度可以到98%， 但是我看网页上提供的数据图片大小是96*112，请问下，LFW处理上应用什么其他方法吗？

    Test Epoch: [5888/6000 (96%)]: : 24it [00:32, 1.34s/it] Accuracy: 0.97383+-0.00675 Best_thresholds: 1.16000 Validation rate: 0.82100+-0.03127 @ FAR=0.00100

    opened by xiaomujiang 1
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