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FaceBagNet - Patch-based Methods for Multi-modal Face Anti-spoofing (FAS)

Python 100.00%

facebagnet's Introduction

Hi 👋, I'm Tao Shen

AI Developer / Kaggle Grandmaster

My Social Media 💬

LinkedIn: @TaoShen
Kaggle Profile: @SeuTao
Twitter: @SeuTao
Email: [email protected]

facebagnet's People

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facebagnet's Issues

ModuleNotFoundError: No module named 'model.backbone.senet'

你好，这部分代码没有吗？

怎么判断图片是否有摩尔纹

模型是利用有摩尔纹跟没有摩尔纹数据集进行训练,这是人为区分出来的.
如果不人为区分,有什么方法可以判断一张图片是否有摩尔纹,是否有什么指标判断摩尔纹程度?

about model

The training input of the model is the whole face,
and the local patches are not reflected

Running models on edge applications

Hi, sorry to bother you but I have a question about coverting the model(.pth) to .pt. I want to run my models on Android but none of them works, so I was wondering do you know how to run my trained models on edge applications?

no module named 'augmentation'

train using CUDA_VISIBLE_DEVICES=0 python train_Fusion_CyclicLR.py --model=model_A --image_size=48

FaceBagNet中的type=A/B/C 三种类型有证明哪种最优么

在定义FaceBagNet时,传参type=A/B/C,三种类型有证明哪种最优么?我看提供的预训练模型只是A的.
另外实际测试中,IR测试,如果图比较亮,则很容易判断为真,比如我对着白纸拍摄,根据rgb扣除人脸,但是测试结果却为真.

About problem of random cropping

In line 86-88 of the process/data_fusion.py, it seems will select the patch of different modalities from different face regions.
I would like to ask is it designed on purpose? Why design it? I didn't see the relevant explanation in the paper.

多模态问题

请问您这个多模态都有哪几个模态呀？

softmax_cross_entropy_criterion

你好，我在train的代码中发现softmax_cross_entropy_criterion，请问这个是什么？网络上也没有搜索到这个的相关内容。

FileNotFoundError: [Errno 2] No such file or directory: '/data1/shentao/DATA/CVPR19_FaceAntiSpoofing/train_list.txt'

(CVPR19) (base) marco@pc:~/antiFaceSpoofing/CVPR19-Face-Anti-spoofing-master$  
  CUDA_VISIBLE_DEVICES=0 python3 train_Fusion_CyclicLR.py --model=model_A 
  --image_size=48
Namespace(batch_size=128, cycle_inter=50, cycle_num=10, image_mode='fusion',  
image_size=48, mode='train', model='model_A', pretrained_model=None, train_fold_index=-1)
    out_dir      = ./models/model_A_fusion_48

    <additional comments>
      ... xxx baseline  ... 

** dataset setting **
fold: -1
fusion
train
fold index set:  -1
Traceback (most recent call last):
  File "train_Fusion_CyclicLR.py", line 254, in <module>
    main(config)
  File "train_Fusion_CyclicLR.py", line 231, in main
    run_train(config)
  File "train_Fusion_CyclicLR.py", line 48, in run_train
    fold_index=config.train_fold_index)
  File "/home/marco/antiFaceSpoofing/CVPR19-Face-Anti-spoofing-master/process
/data_fusion.py", line 22, in __init__
    self.set_mode(self.mode,self.fold_index)
  File "/home/marco/antiFaceSpoofing/CVPR19-Face-Anti-spoofing-master/process
/data_fusion.py", line 41, in set_mode
    self.train_list = load_train_list()
  File "/home/marco/antiFaceSpoofing/CVPR19-Face-Anti-spoofing-master/process
/data_helper.py", line 13, in load_train_list
    f = open(DATA_ROOT + '/train_list.txt')
FileNotFoundError: [Errno 2] No such file or directory: '/data1/shentao
  /DATA/CVPR19_FaceAntiSpoofing/train_list.txt'

have test model？？？

error: (-215:Assertion failed) !ssize.empty() in function 'resize'

作者你好，感谢你的开源，但我在运行代码的时候遇到了如下问题，请问你可以给我一些指导吗？谢谢～
Traceback (most recent call last):
in getitem
image = cv2.resize(image,(RESIZE_SIZE,RESIZE_SIZE))
cv2.error: OpenCV(3.4.2) /tmp/build/80754af9/opencv-suite_1535558553474/work/modules/imgproc/src/resize.cpp:4044: error: (-215:Assertion failed) !ssize.empty() in function 'resize'

The ConvMixer MLPMixer VisionPermutator ViT model can export onnx and run the edge device.

请问有大佬能跑通吗？需要改代码吗？

改完一个error又出现一个error，这份代码能正常跑吗？

如何进行推理，检测一张图片是否为活体

如题！！！

can not find ModuleNotFoundError: No module named 'model.backbone.senet' model

please

Transfer learning: shape mismatch in two keys when load_pretrain()

Hello hello!

Congrats on the outstanding project!!

I am trying to load the pretrained models provided but I run into the same shape mismatch error, always in keys encoder.last_linear.weight and encoder.last_linear.bias, no matter which .pth.tar I try to load (IR, depth or RGB; 32, 48 or 64).

The code:

bobo_A = Net()
path = "\model_A_color_64\checkpoint\global_min_acer_model.pth.tar"
bobo_A.load_pretrain(path)

Raises:

RuntimeError: Error(s) in loading state_dict for Net:
	size mismatch for encoder.last_linear.weight: copying a param with shape torch.Size([1000, 2048]) from checkpoint, the shape in current model is torch.Size([2, 2048]).
	size mismatch for encoder.last_linear.bias: copying a param with shape torch.Size([1000]) from checkpoint, the shape in current model is torch.Size([2]).

I am not being able to find a workaround. Any help?

Thanks!

Because the challenge is still on going and there are no released results.
I want to find good solution for anti-spoofing so i want to know you got 2nd place or something in the challenge.

Thanks for your contribution