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

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
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    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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