Pretrained Pytorch face detection (MTCNN) and recognition (InceptionResnet) models

Foo

Face Recognition Using Pytorch

Downloads

Code Coverage

Python 3.7 3.6 3.5
Status Build Status Build Status Build Status

xscode

This is a repository for Inception Resnet (V1) models in pytorch, pretrained on VGGFace2 and CASIA-Webface.

Pytorch model weights were initialized using parameters ported from David Sandberg's tensorflow facenet repo.

Also included in this repo is an efficient pytorch implementation of MTCNN for face detection prior to inference. These models are also pretrained. To our knowledge, this is the fastest MTCNN implementation available.

Table of contents

Quick start

  1. Install:

    # With pip:
    pip install facenet-pytorch
    
    # or clone this repo, removing the '-' to allow python imports:
    git clone https://github.com/timesler/facenet-pytorch.git facenet_pytorch
    
    # or use a docker container (see https://github.com/timesler/docker-jupyter-dl-gpu):
    docker run -it --rm timesler/jupyter-dl-gpu pip install facenet-pytorch && ipython
  2. In python, import facenet-pytorch and instantiate models:

    from facenet_pytorch import MTCNN, InceptionResnetV1
    
    # If required, create a face detection pipeline using MTCNN:
    mtcnn = MTCNN(image_size=<image_size>, margin=<margin>)
    
    # Create an inception resnet (in eval mode):
    resnet = InceptionResnetV1(pretrained='vggface2').eval()
  3. Process an image:

    from PIL import Image
    
    img = Image.open(<image path>)
    
    # Get cropped and prewhitened image tensor
    img_cropped = mtcnn(img, save_path=<optional save path>)
    
    # Calculate embedding (unsqueeze to add batch dimension)
    img_embedding = resnet(img_cropped.unsqueeze(0))
    
    # Or, if using for VGGFace2 classification
    resnet.classify = True
    img_probs = resnet(img_cropped.unsqueeze(0))

See help(MTCNN) and help(InceptionResnetV1) for usage and implementation details.

Pretrained models

See: models/inception_resnet_v1.py

The following models have been ported to pytorch (with links to download pytorch state_dict's):

Model name LFW accuracy (as listed here) Training dataset
20180408-102900 (111MB) 0.9905 CASIA-Webface
20180402-114759 (107MB) 0.9965 VGGFace2

There is no need to manually download the pretrained state_dict's; they are downloaded automatically on model instantiation and cached for future use in the torch cache. To use an Inception Resnet (V1) model for facial recognition/identification in pytorch, use:

from facenet_pytorch import InceptionResnetV1

# For a model pretrained on VGGFace2
model = InceptionResnetV1(pretrained='vggface2').eval()

# For a model pretrained on CASIA-Webface
model = InceptionResnetV1(pretrained='casia-webface').eval()

# For an untrained model with 100 classes
model = InceptionResnetV1(num_classes=100).eval()

# For an untrained 1001-class classifier
model = InceptionResnetV1(classify=True, num_classes=1001).eval()

Both pretrained models were trained on 160x160 px images, so will perform best if applied to images resized to this shape. For best results, images should also be cropped to the face using MTCNN (see below).

By default, the above models will return 512-dimensional embeddings of images. To enable classification instead, either pass classify=True to the model constructor, or you can set the object attribute afterwards with model.classify = True. For VGGFace2, the pretrained model will output logit vectors of length 8631, and for CASIA-Webface logit vectors of length 10575.

Example notebooks

Complete detection and recognition pipeline

Face recognition can be easily applied to raw images by first detecting faces using MTCNN before calculating embedding or probabilities using an Inception Resnet model. The example code at examples/infer.ipynb provides a complete example pipeline utilizing datasets, dataloaders, and optional GPU processing.

Face tracking in video streams

MTCNN can be used to build a face tracking system (using the MTCNN.detect() method). A full face tracking example can be found at examples/face_tracking.ipynb.

Finetuning pretrained models with new data

In most situations, the best way to implement face recognition is to use the pretrained models directly, with either a clustering algorithm or a simple distance metrics to determine the identity of a face. However, if finetuning is required (i.e., if you want to select identity based on the model's output logits), an example can be found at examples/finetune.ipynb.

Guide to MTCNN in facenet-pytorch

This guide demonstrates the functionality of the MTCNN module. Topics covered are:

  • Basic usage
  • Image normalization
  • Face margins
  • Multiple faces in a single image
  • Batched detection
  • Bounding boxes and facial landmarks
  • Saving face datasets

See the notebook on kaggle.

Performance comparison of face detection packages

This notebook demonstrates the use of three face detection packages:

  1. facenet-pytorch
  2. mtcnn
  3. dlib

Each package is tested for its speed in detecting the faces in a set of 300 images (all frames from one video), with GPU support enabled. Performance is based on Kaggle's P100 notebook kernel. Results are summarized below.

Package FPS (1080x1920) FPS (720x1280) FPS (540x960)
facenet-pytorch 12.97 20.32 25.50
facenet-pytorch (non-batched) 9.75 14.81 19.68
dlib 3.80 8.39 14.53
mtcnn 3.04 5.70 8.23

See the notebook on kaggle.

The FastMTCNN algorithm

This algorithm demonstrates how to achieve extremely efficient face detection specifically in videos, by taking advantage of similarities between adjacent frames.

See the notebook on kaggle.

Running with docker

The package and any of the example notebooks can be run with docker (or nvidia-docker) using:

docker run --rm -p 8888:8888
    -v ./facenet-pytorch:/home/jovyan timesler/jupyter-dl-gpu \
    -v <path to data>:/home/jovyan/data
    pip install facenet-pytorch && jupyter lab 

Navigate to the examples/ directory and run any of the ipython notebooks.

See timesler/jupyter-dl-gpu for docker container details.

Use this repo in your own git project

To use this code in your own git repo, I recommend first adding this repo as a submodule. Note that the dash ('-') in the repo name should be removed when cloning as a submodule as it will break python when importing:

git submodule add https://github.com/timesler/facenet-pytorch.git facenet_pytorch

Alternatively, the code can be installed as a package using pip:

pip install facenet-pytorch

Conversion of parameters from Tensorflow to Pytorch

See: models/utils/tensorflow2pytorch.py

Note that this functionality is not needed to use the models in this repo, which depend only on the saved pytorch state_dict's.

Following instantiation of the pytorch model, each layer's weights were loaded from equivalent layers in the pretrained tensorflow models from davidsandberg/facenet.

The equivalence of the outputs from the original tensorflow models and the pytorch-ported models have been tested and are identical:


>>> compare_model_outputs(mdl, sess, torch.randn(5, 160, 160, 3).detach())

Passing test data through TF model

tensor([[-0.0142,  0.0615,  0.0057,  ...,  0.0497,  0.0375, -0.0838],
        [-0.0139,  0.0611,  0.0054,  ...,  0.0472,  0.0343, -0.0850],
        [-0.0238,  0.0619,  0.0124,  ...,  0.0598,  0.0334, -0.0852],
        [-0.0089,  0.0548,  0.0032,  ...,  0.0506,  0.0337, -0.0881],
        [-0.0173,  0.0630, -0.0042,  ...,  0.0487,  0.0295, -0.0791]])

Passing test data through PT model

tensor([[-0.0142,  0.0615,  0.0057,  ...,  0.0497,  0.0375, -0.0838],
        [-0.0139,  0.0611,  0.0054,  ...,  0.0472,  0.0343, -0.0850],
        [-0.0238,  0.0619,  0.0124,  ...,  0.0598,  0.0334, -0.0852],
        [-0.0089,  0.0548,  0.0032,  ...,  0.0506,  0.0337, -0.0881],
        [-0.0173,  0.0630, -0.0042,  ...,  0.0487,  0.0295, -0.0791]],
       grad_fn=<DivBackward0>)

Distance 1.2874517096861382e-06

In order to re-run the conversion of tensorflow parameters into the pytorch model, ensure you clone this repo with submodules, as the davidsandberg/facenet repo is included as a submodule and parts of it are required for the conversion.

References

  1. David Sandberg's facenet repo: https://github.com/davidsandberg/facenet

  2. F. Schroff, D. Kalenichenko, J. Philbin. FaceNet: A Unified Embedding for Face Recognition and Clustering, arXiv:1503.03832, 2015. PDF

  3. Q. Cao, L. Shen, W. Xie, O. M. Parkhi, A. Zisserman. VGGFace2: A dataset for recognising face across pose and age, International Conference on Automatic Face and Gesture Recognition, 2018. PDF

  4. D. Yi, Z. Lei, S. Liao and S. Z. Li. CASIAWebface: Learning Face Representation from Scratch, arXiv:1411.7923, 2014. PDF

  5. K. Zhang, Z. Zhang, Z. Li and Y. Qiao. Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks, IEEE Signal Processing Letters, 2016. PDF

Owner
Tim Esler
ML, Data Science, AI, Physics, Computational Neuroscience, Biomedical Engineering
Tim Esler
Comments
  • memory leak

    memory leak

    Hello, Im facing a memory leak and I can't find out why. I am simply looping through a lot of images and it gradually fills all my memory, this is my setup: Version facenet-pytorch==2.0.1

    mtcnn = MTCNN(image_size=64, keep_all=True)
    resnet = InceptionResnetV1(pretrained='vggface2').eval()
    
    for nth, img_path in enumerate(img_paths):
        img = Image.open(img_path.resolve())
        boxes, probs = mtcnn.detect(img)
    
  • Loading TorchScript Module : class method not recognized during compilation

    Loading TorchScript Module : class method not recognized during compilation

    I know it's not strictly related to facenet-pytorch library, but I do hope that may be you or others can give me an help.

    My objective is to use the following class which simply uses your excellent work:

    from facenet_pytorch import MTCNN, InceptionResnetV1
    import torch
    from torch.utils.data import DataLoader
    from torchvision import datasets
    import numpy as np
    import pandas as pd
    import os
    
    workers = 0 if os.name == 'nt' else 4
    
    def collate_fn(x):
        return x[0]
    
    def describe(x):
        print("Type: {}".format(x.type()))
        print("Shape/size: {}".format(x.shape))
        print("Values: \n{}".format(x))
    
    class GetFaceEmbedding(torch.nn.Module):
        def __init__(self):
            super(GetFaceEmbedding, self).__init__()
    
        @classmethod
        def getFaceEmbedding(self, imagePath):
    
            device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
            print('Running on device: {}'.format(device))
    
            mtcnn = MTCNN(
                image_size=160, margin=0, min_face_size=20,
                thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True,
                device=device
            )
            resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device)
            dataset = datasets.ImageFolder(imagePath)
            dataset.idx_to_class = {i:c for c, i in dataset.class_to_idx.items()}
            loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=workers)
            aligned = []
            names = []
            for x, y in loader:
                x_aligned, prob = mtcnn(x, return_prob=True)
                if x_aligned is not None:
                    print('Face detected with probability: {:8f}'.format(prob))
                    aligned.append(x_aligned)
                    names.append(dataset.idx_to_class[y])
            aligned = torch.stack(aligned).to(device)
            embeddings = resnet(aligned).detach().cpu()
            return embeddings
    

    With python it works fine:

    (venv373) (base) [email protected]:~/PyTorchMatters/facenet_pytorch/examples$ python3 .  
    /getFaceEmbedding-01.py 
    Running on device: cpu
    Face detected with probability: 0.999430
    Type: torch.FloatTensor
    Shape/size: torch.Size([1, 512])
    Values: 
    tensor([[ 3.6307e-02, -8.8092e-02, -3.5002e-02, -8.2932e-02,  1.9032e-02,
              2.3228e-02,  2.4253e-02, -3.7844e-02, -6.8906e-02,  2.0351e-02,
             -6.7093e-02,  3.6181e-02, -2.5933e-02, -6.0015e-02,  2.6653e-02,
              9.4335e-02, -2.9241e-02, -2.8357e-02,  7.2207e-02, -3.7747e-02,
              6.3515e-03, -3.0220e-02, -2.4530e-02,  1.0004e-01,  6.6520e-02,
              ....
              3.2497e-02,  2.3421e-02, -5.3921e-02,  1.9589e-02, -2.8655e-03,
              1.3474e-02, -2.2743e-02,  3.2976e-02, -5.6658e-02,  2.0837e-02,
            -4.7152e-02, -6.5534e-02]])
    

    Following the indications found here: https://pytorch.org/tutorials/advanced/cpp_export.html

    I added to getFaceEmbedding.py these lines:

    my_module = GetFaceEmbedding()
    sm = torch.jit.script(my_module)
    sm.save("annotated_get_face_embedding.pt")
    

    I then saved the serialized file:

    (venv373) (base) [email protected]:~/PyTorchMatters/facenet_pytorch/examples$ python3 
    ./getFaceEmbedding.py
    
    -rw-r--r-- 1 marco marco 1,4K mar 19 18:52 annotated_get_face_embedding.pt
    

    And created this cpp file:

    (venv373) (base) [email protected]:~/PyTorchMatters/facenet_pytorch/examples$ nano 
    faceEmbedding.cpp
    
    #include <torch/script.h>
    #include <iostream>
    #include <memory>
    #include <filesystem>
    
    int main(int argc, const char* argv[]) {
        //if(argc != 3) {
        //    std::cerr << "usage:usage: faceEmbedding <path-to-exported-script-module> <path-to-
       // image-file> \n";
        //    return -1;
        //}
    
      torch::jit::script::Module module;
      try {
          // Deserialize the ScriptModule from a file using torch::jit::load().
          module = torch::jit::load(argv[1]);
          std::filesystem::path imgPath = argv[2];
    
          // Execute the model and turn its output into a tensor
          at::Tensor output = module.getFaceEmbedding(imgPath).ToTensor();
      }
      catch (const c10::Error& e) {
          std::cerr << "error loading the model\n";
          return -1;
      }
      std::cout << "ok\n";
    } // end of main() function
    

    But during the compilation phase I get this error :

    (venv373) (base) [email protected]:~/PyTorchMatters/facenet_pytorch/examples$ mkdir build
    (venv373) (base) [email protected]:~/PyTorchMatters/facenet_pytorch/examples$ cd build
    (venv373) (base) [email protected]:~/PyTorchMatters/facenet_pytorch/examples/build$ cmake 
    -DCMAKE_PREFIX_PATH=/home/marco/PyTorchMatters/libtorch ..
    -- The C compiler identification is GNU 9.2.1
    -- The CXX compiler identification is GNU 9.2.1
    -- Check for working C compiler: /usr/bin/cc
    -- Check for working C compiler: /usr/bin/cc -- works
    -- Detecting C compiler ABI info
    -- Detecting C compiler ABI info - done
    -- Detecting C compile features
    -- Detecting C compile features - done
    -- Check for working CXX compiler: /usr/bin/c++
    -- Check for working CXX compiler: /usr/bin/c++ -- works
    -- Detecting CXX compiler ABI info
    -- Detecting CXX compiler ABI info - done
    -- Detecting CXX compile features
    -- Detecting CXX compile features - done
    -- Looking for pthread.h
    -- Looking for pthread.h - found
    -- Performing Test CMAKE_HAVE_LIBC_PTHREAD
    -- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Failed
    -- Looking for pthread_create in pthreads
    -- Looking for pthread_create in pthreads - not found
    -- Looking for pthread_create in pthread
    -- Looking for pthread_create in pthread - found
    -- Found Threads: TRUE  
    -- Found torch: /home/marco/PyTorchMatters/libtorch/lib/libtorch.so  
    -- Configuring done
    -- Generating done
    -- Build files have been written to: /home/marco/PyTorchMatters/facenet_pytorch/examples/build
    
    (venv373) (base) [email protected]:~/PyTorchMatters/facenet_pytorch/examples/build$ cmake --build 
    . 
    --config Release
    Scanning dependencies of target faceEmbedding
    [ 50%] Building CXX object CMakeFiles/faceEmbedding.dir/faceEmbedding.cpp.o
        /home/marco/PyTorchMatters/facenet_pytorch/examples/faceEmbedding.cpp: In function ‘int 
    main(int, const char**)’:
    /home/marco/PyTorchMatters/facenet_pytorch/examples/faceEmbedding.cpp:20:34: error: ‘struct 
    torch::jit::script::Module’ has no member named ‘getFaceEmbedding’
       20 |       at::Tensor output = module.getFaceEmbedding(imgPath).ToTensor();
          |                                  ^~~~~~~~~~~~~~~~
    CMakeFiles/faceEmbedding.dir/build.make:62: recipe for target 'CMakeFiles/faceEmbedding.dir
    /faceEmbedding.cpp.o' failed
    make[2]: *** [CMakeFiles/faceEmbedding.dir/faceEmbedding.cpp.o] Error 1
    CMakeFiles/Makefile2:75: recipe for target 'CMakeFiles/faceEmbedding.dir/all' failed
    make[1]: *** [CMakeFiles/faceEmbedding.dir/all] Error 2
    Makefile:83: recipe for target 'all' failed
    make: *** [all] Error 2
    

    How to solve the problem? Looking forward to your kind help. Marco

  • how to setup the pretrained model locally?

    how to setup the pretrained model locally?

    since the internet is too slow to download and frequently disconnected.

    I could get the pretained model files some other way.

    so how should I set it up locally?

    Thank you!

  • how to upload my own dataset ?

    how to upload my own dataset ?

    thanks for this repo , i've a problem , how to upload my own dataset for resnet? does inception resnet with resnet are the same thing ?! im new to nn thanks for your advice ..

  • VGGFace2 fine-tune: Poor training accuracy on CPU

    VGGFace2 fine-tune: Poor training accuracy on CPU

    I'm using this code to fine-tune the model but I'm getting very poor training accuracy, could you please point out what could be the issue:

    # define mtcnn
    mtcnn = MTCNN(
        image_size=160, margin=0, min_face_size=20,
        thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True,
        device=device
    )
    
    # load data
    dataset = datasets.ImageFolder(data_dir, transform=transforms.Resize((512, 512)))
    
    dataset.samples = [
        (p, p.replace(data_dir, data_dir + '_cropped'))
            for p, _ in dataset.samples
    ]
    
    loader = DataLoader(
        dataset,
        num_workers=workers,
        batch_size=batch_size,
        collate_fn=training.collate_pil
    )
    
    print('applying mtcnn...')
    
    # apply mtcnn
    for i, (x, y) in enumerate(loader):
        mtcnn(x, save_path=y)
        print('\rBatch {} of {}'.format(i + 1, len(loader)), end='')
        
    # Remove mtcnn to reduce GPU memory usage
    del mtcnn
    
    print('')
    print('starting training...')
    
    print(len(dataset.class_to_idx))
    
    resnet = InceptionResnetV1(
        classify=False,
        pretrained='vggface2',
        num_classes=len(dataset.class_to_idx)
    ).to(device)
    
    optimizer = optim.Adam(resnet.parameters(), lr=0.001)
    scheduler = MultiStepLR(optimizer, [5, 10])
    
    trans = transforms.Compose([
        np.float32,
        transforms.ToTensor(),
        fixed_image_standardization
    ])
    
    dataset = datasets.ImageFolder(data_dir + '_cropped', transform=trans)
    img_inds = np.arange(len(dataset))
    np.random.shuffle(img_inds)
    train_inds = img_inds[:int(0.8 * len(img_inds))]
    val_inds = img_inds[int(0.8 * len(img_inds)):]
    
    train_loader = DataLoader(
        dataset,
        num_workers=workers,
        batch_size=batch_size,
        sampler=SubsetRandomSampler(train_inds)
    )
    
    val_loader = DataLoader(
        dataset,
        num_workers=workers,
        batch_size=batch_size,
        sampler=SubsetRandomSampler(val_inds)
    )
    
    loss_fn = torch.nn.CrossEntropyLoss()
    metrics = {
        'fps': training.BatchTimer(),
        'acc': training.accuracy
    }
    
    writer = SummaryWriter()
    writer.iteration, writer.interval = 0, 10
    
    print('\n\nInitial')
    print('-' * 10)
    resnet.eval()
    training.pass_epoch(
        resnet, loss_fn, val_loader,
        batch_metrics=metrics, show_running=True, device=device,
        writer=writer
    )
    
    for epoch in range(epochs):
        print('\nEpoch {}/{}'.format(epoch + 1, epochs))
        print('-' * 10)
    
        resnet.train()
        training.pass_epoch(
            resnet, loss_fn, train_loader, optimizer, scheduler,
            batch_metrics=metrics, show_running=True, device=device,
            writer=writer
        )
    
        resnet.eval()
        training.pass_epoch(
            resnet, loss_fn, val_loader,
            batch_metrics=metrics, show_running=True, device=device,
            writer=writer
        )
    
    writer.close()
    
    # save trained model
    torch.save(resnet.state_dict(), trained_model)
    

    and this is the accuracy I'm getting after 8 epochs:

    Initial

    Valid | 1/1 | loss: 6.2234 | fps: 1.4201 | acc: 0.0000

    Epoch 1/8

    Train | 1/1 | loss: 6.2504 | fps: 0.8825 | acc: 0.0000
    Valid | 1/1 | loss: 6.2631 | fps: 1.5254 | acc: 0.0000

    Epoch 2/8

    Train | 1/1 | loss: 6.1771 | fps: 1.3746 | acc: 0.0000
    Valid | 1/1 | loss: 6.2687 | fps: 1.3014 | acc: 0.0000

    Epoch 3/8

    Train | 1/1 | loss: 6.1627 | fps: 1.0087 | acc: 0.2500
    Valid | 1/1 | loss: 6.2471 | fps: 1.5138 | acc: 0.0000

    Epoch 4/8

    Train | 1/1 | loss: 6.1570 | fps: 1.0297 | acc: 0.2500
    Valid | 1/1 | loss: 6.2371 | fps: 1.8226 | acc: 0.0000

    Epoch 5/8

    Train | 1/1 | loss: 6.1445 | fps: 0.8727 | acc: 0.5000
    Valid | 1/1 | loss: 6.2335 | fps: 0.9244 | acc: 0.0000

    Epoch 6/8

    Train | 1/1 | loss: 6.1274 | fps: 1.1550 | acc: 0.5000
    Valid | 1/1 | loss: 6.2234 | fps: 1.6978 | acc: 0.0000

    Epoch 7/8

    Train | 1/1 | loss: 6.1252 | fps: 1.4416 | acc: 0.2500
    Valid | 1/1 | loss: 6.1999 | fps: 1.7895 | acc: 0.0000

    Epoch 8/8

    Train | 1/1 | loss: 6.1179 | fps: 1.4245 | acc: 0.5000
    Valid | 1/1 | loss: 6.1874 | fps: 1.6070 | acc: 0.0000

  • InceptionResnetV1: UnpicklingError: invalid load key, '<'.

    InceptionResnetV1: UnpicklingError: invalid load key, '<'.

    Hi! This repo is one of the best libraries to perform the task. I have been using your package for the last few weeks on a daily basis without encountering any issues. However, since the last few hours (3 July 2020 GMT+5:30), upon instantiating the resnet model with pretrained models (both, vggface2 and casia-webface) in the following way: resnet = InceptionResnetV1(pretrained='vggface2') The following error is being generated on every run:

    ---------------------------------------------------------------------------
    UnpicklingError                           Traceback (most recent call last)
    <ipython-input-76-a829445a43a7> in <module>()
    ----> 1     resnet = InceptionResnetV1(pretrained='vggface2')
    
    3 frames
    /usr/local/lib/python3.6/dist-packages/facenet_pytorch/models/inception_resnet_v1.py in __init__(self, pretrained, classify, num_classes, dropout_prob, device)
        259 
        260         if pretrained is not None:
    --> 261             load_weights(self, pretrained)
        262 
        263         if self.num_classes is not None:
    
    /usr/local/lib/python3.6/dist-packages/facenet_pytorch/models/inception_resnet_v1.py in load_weights(mdl, name)
        334             with open(cached_file, 'wb') as f:
        335                 f.write(r.content)
    --> 336         state_dict.update(torch.load(cached_file))
        337 
        338     mdl.load_state_dict(state_dict)
    
    /usr/local/lib/python3.6/dist-packages/torch/serialization.py in load(f, map_location, pickle_module, **pickle_load_args)
        591                     return torch.jit.load(f)
        592                 return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
    --> 593         return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
        594 
        595 
    
    /usr/local/lib/python3.6/dist-packages/torch/serialization.py in _legacy_load(f, map_location, pickle_module, **pickle_load_args)
        761             "functionality.".format(type(f)))
        762 
    --> 763     magic_number = pickle_module.load(f, **pickle_load_args)
        764     if magic_number != MAGIC_NUMBER:
        765         raise RuntimeError("Invalid magic number; corrupt file?")
    
    UnpicklingError: invalid load key, '<'.
    

    My guess is that the pretrained model files were changed/corrupted. Please have a look at it.

  • Use it as a feature extrator

    Use it as a feature extrator

    Hi Tim, I would like to use this facenet as a feature extractor, i.e., delete the last few FC layers. My Code model = InceptionResnetV1(pretrained='vggface2', num_classes=8, classify=True).to(device) print(model) Result of last few layers: (avgpool_1a): AdaptiveAvgPool2d(output_size=1) (dropout): Dropout(p=0.6, inplace=False) (last_linear): Linear(in_features=1792, out_features=512, bias=False) (last_bn): BatchNorm1d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (logits): Linear(in_features=512, out_features=8, bias=True) )' Then, I delete the last FC and BN, so I do the following model = nn.Sequential(*list(model.children())[:-2]) print("After Delete") print(model) Result of last few layers: (13): AdaptiveAvgPool2d(output_size=1) (14): Dropout(p=0.6, inplace=False) (15): Linear(in_features=1792, out_features=512, bias=False)

    Yes. This architecture is what I want.

    But, if I want to use this pretrained weighted to get a 512d output, then I have the following error: File "/home/xxx/anaconda3/envs/face_env/lib/python3.7/site/packages/torch/nn/functional.py", line 1371, in linear output = input.matmul(weight.t()) RuntimeError: size mismatch, m1: [3584 x 1], m2: [1792 x 512] at /opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/THC/generic/THCTensorMathBlas.cu:273

  • Add methods for model training

    Add methods for model training

    The repo currently only includes models (MTCNN and InceptionResnetV1). It would be good to add code for updating pretrained models given new data or different training hyperparameters.

  • TypeError: len() of unsized object

    TypeError: len() of unsized object

    I get this error when I read the image with PIL and even cv2. Traceback (most recent call last): File "preprocessing_siwm.py", line 81, in <module> mtcnn_caller(new_testset_path, image_folder+'/',image) File "preprocessing_siwm.py", line 49, in mtcnn_caller img_cropped = mtcnn(img, save_path=path+'spoof/'+image+'/'+image+'.png') File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(*input, **kwargs) File "/usr/local/lib/python3.6/dist-packages/facenet_pytorch/models/mtcnn.py", line 262, in forward batch_boxes, batch_probs, batch_points, img, method=self.selection_method File "/usr/local/lib/python3.6/dist-packages/facenet_pytorch/models/mtcnn.py", line 408, in select_boxes if len(boxes) == 0: TypeError: len() of unsized object I tried checking if the image existed using if img is not None: But that doesn't help. Please guide me if I'm missing on something. Thanks a lot.

  • Compatibility for ndarray batches of images

    Compatibility for ndarray batches of images

    It was not possible to pass the mtcnn forward method a batch of np ndarrays images (4D images) stacked on the first dimension, even if described as possible in the documentation and working with the detection methods already implemented. I propose a little change to make it possible. I have tested it against a batch of np ndarray images, a list of ndarrays images, a list of PIL images, single PIL and np ndarrays images and it is working. Great package, thank you!

  • Fine-tuning & pre-whitening

    Fine-tuning & pre-whitening

    Thanks @timesler for the great work! I was trying to fine-tune on a small dataset and noticed that the network overfits very quickly. I think it's useful to train only the last linear layer ("logits" in your code). This worked fine for me:

    optimizer = optim.Adam(net.module.logits.parameters())
    

    Unrelated-ly, the pre-whiten transform may be screwing up the identification a bit as it normalizes each face using its own mean & std-dev. In one of my test videos, an African guy is identified as a white guy. So using a standard pre-processing for all faces may be a good idea. I see there's a related pull-request. Maybe I will experiment a bit myself next week.

    Thanks again.

  • 「...'aten::nonzero' is not currently supported...」on M1

    「...'aten::nonzero' is not currently supported...」on M1

    Problem: muti error occurred when I try to run the example from help(MTCNN) on my MacBook Air M1. Env: macOS Monterey 12.6 MacBook Air(M1, 2020) 8GB RAM iBoot 7459.141.1

    conda 4.13.0 Python 3.8.13 # packages in environment at /opt/homebrew/anaconda3/envs/pytorch: # # Name Version Build Channel bzip2 1.0.8 h3422bc3_4 conda-forge ca-certificates 2022.9.24 h4653dfc_0 conda-forge certifi 2022.9.24 pypi_0 pypi charset-normalizer 2.1.1 pypi_0 pypi facenet-pytorch 2.5.2 pypi_0 pypi idna 3.4 pypi_0 pypi libffi 3.4.2 h3422bc3_5 conda-forge libsqlite 3.39.4 h76d750c_0 conda-forge libzlib 1.2.12 h03a7124_4 conda-forge ncurses 6.3 h07bb92c_1 conda-forge numpy 1.23.4 pypi_0 pypi opencv-python 4.6.0.66 pypi_0 pypi openssl 3.0.5 h03a7124_2 conda-forge pillow 9.2.0 pypi_0 pypi pip 22.2.2 pyhd8ed1ab_0 conda-forge python 3.8.13 hd3575e6_0_cpython conda-forge readline 8.1.2 h46ed386_0 conda-forge requests 2.28.1 pypi_0 pypi setuptools 65.4.1 pyhd8ed1ab_0 conda-forge sqlite 3.39.4 h2229b38_0 conda-forge tk 8.6.12 he1e0b03_0 conda-forge torch 1.14.0.dev20221012 pypi_0 pypi torchvision 0.15.0.dev20221012 pypi_0 pypi typing-extensions 4.4.0 pypi_0 pypi urllib3 1.26.12 pypi_0 pypi wheel 0.37.1 pyhd8ed1ab_0 conda-forge xz 5.2.6 h57fd34a_0 conda-forge

    Result: (pytorch) Running on device: mps /opt/homebrew/anaconda3/envs/pytorch/lib/python3.8/site-packages/facenet_pytorch/models/utils/detect_face.py:210: UserWarning: The operator 'aten::nonzero' is not currently supported on the MPS backend and will fall back to run on the CPU. This may have performance implications. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:11.) mask_inds = mask.nonzero() Traceback (most recent call last): File "facenetMTCNN_example.py", line 13, in <module> boxes, probs, points = mtcnn.detect(img, landmarks=True) File "/opt/homebrew/anaconda3/envs/pytorch/lib/python3.8/site-packages/facenet_pytorch/models/mtcnn.py", line 313, in detect batch_boxes, batch_points = detect_face( File "/opt/homebrew/anaconda3/envs/pytorch/lib/python3.8/site-packages/facenet_pytorch/models/utils/detect_face.py", line 79, in detect_face pick = batched_nms(boxes_scale[:, :4], boxes_scale[:, 4], image_inds_scale, 0.5) File "/opt/homebrew/anaconda3/envs/pytorch/lib/python3.8/site-packages/torchvision/ops/boxes.py", line 75, in batched_nms return _batched_nms_coordinate_trick(boxes, scores, idxs, iou_threshold) File "/opt/homebrew/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/jit/_trace.py", line 1136, in wrapper return fn(*args, **kwargs) File "/opt/homebrew/anaconda3/envs/pytorch/lib/python3.8/site-packages/torchvision/ops/boxes.py", line 94, in _batched_nms_coordinate_trick keep = nms(boxes_for_nms, scores, iou_threshold) File "/opt/homebrew/anaconda3/envs/pytorch/lib/python3.8/site-packages/torchvision/ops/boxes.py", line 41, in nms return torch.ops.torchvision.nms(boxes, scores, iou_threshold) File "/opt/homebrew/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/_ops.py", line 442, in __call__ return self._op(*args, **kwargs or {}) NotImplementedError: The operator 'torchvision::nms' is not currently implemented for the MPS device. If you want this op to be added in priority during the prototype phase of this feature, please comment on https://github.com/pytorch/pytorch/issues/77764. As a temporary fix, you can set the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be slower than running natively on MPS.

    Here's the code: from PIL import Image, ImageDraw from facenet_pytorch import MTCNN, extract_face import cv2 import torch

    device = 'mps' if torch.backends.mps.is_available() and torch.backends.mps.is_built() else 'cpu' print("Running on device: {}".format(device))

    #cap = cv2.VideoCapture(0) img = Image.open('./photos/Chris/16871.jpg')

    mtcnn = MTCNN(keep_all=True, device=device) boxes, probs, points = mtcnn.detect(img, landmarks=True) print(boxes, probs, points)

    img_draw = img.copy() draw = ImageDraw.Draw(img_draw) for i, (box, point) in enumerate(zip(boxes, points)):     draw.rectangle(box.tolist(), width=5)     for p in point:         draw.rectangle((p - 10).tolist() + (p + 10).tolist(), width=10)     extract_face(img, box, save_path='detected_face_{}.png'.format(i)) img_draw.save('annotated_faces.png')

  • ONNX conversion

    ONNX conversion

    Has anybody successfully converted the MTCNN to ONNX? Keep getting the error:

    ~\Anaconda3\envs\facenet\lib\site-packages\facenet_pytorch\models\utils\detect_face.py in detect_face(imgs, minsize, pnet, rnet, onet, threshold, factor, device)
         81         offset += boxes_scale.shape[0]
         82 
    ---> 83     boxes = torch.cat(boxes, dim=0)
         84     image_inds = torch.cat(image_inds, dim=0)
         85 
    
    RuntimeError: torch.cat(): expected a non-empty list of Tensors
    
  • output embeddings dimension is weird

    output embeddings dimension is weird

    my input is torch.Size([1, 3, 160, 160]). Why is the output dimensions torch.Size([1, 1792, 3, 3]) and not 512?

    I initialize the model like this - resnet = InceptionResnetV1('vggface2').eval()

  • Unexpected EOF Error for Resnet

    Unexpected EOF Error for Resnet

    Running the following code produces an "Unexpected end of file" error:

    resnet = InceptionResnetV1(pretrained='vggface2').eval()

    Traceback (most recent call last): File "<pyshell#4>", line 1, in resnet = InceptionResnetV1(pretrained='vggface2').eval() File "C:\Users\AVezey\AppData\Local\Programs\Python\Python310\lib\site-packages\facenet_pytorch\models\inception_resnet_v1.py", line 262, in init load_weights(self, pretrained) File "C:\Users\AVezey\AppData\Local\Programs\Python\Python310\lib\site-packages\facenet_pytorch\models\inception_resnet_v1.py", line 329, in load_weights state_dict = torch.load(cached_file) File "C:\Users\AVezey\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\serialization.py", line 713, in load return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args) File "C:\Users\AVezey\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\serialization.py", line 938, in _legacy_load typed_storage._storage._set_from_file( RuntimeError: unexpected EOF, expected 386827 more bytes. The file might be corrupted.

  • numpy throwing deprecation warning for creating ndarray from nested s…

    numpy throwing deprecation warning for creating ndarray from nested s…

    Numpy is throwing deprecation warnings for creating arrays from nested sequences on line 183 of detect_face.py and on lines 339, 340, 341 of mtcnn.py when running the example training script. The fix is to pass 'dtype=object' as a parameter when creating the ndarray. E.g., on line 339 of mtcnn.py np.array(boxes) becomes np.array(boxes, dtype=object)

Face Detection and Alignment using Multi-task Cascaded Convolutional Networks (MTCNN)
Face Detection and Alignment using Multi-task Cascaded Convolutional Networks (MTCNN)

Face-Detection-with-MTCNN Face detection is a computer vision problem that involves finding faces in photos. It is a trivial problem for humans to sol

Oct 7, 2022
In this project, we develop a face recognize platform based on MTCNN object-detection netcwork and FaceNet self-supervised network.
In this project, we develop a face recognize platform based on MTCNN object-detection netcwork and FaceNet self-supervised network.

模式识别大作业——人脸检测与识别平台 本项目是一个简易的人脸检测识别平台,提供了人脸信息录入和人脸识别的功能。前端采用 html+css+js,后端采用 pytorch,

Aug 2, 2022
Face-Recognition-Attendence-System - This face recognition Attendence system using Python

Face-Recognition-Attendence-System I have developed this face recognition Attend

May 10, 2022
Face Library is an open source package for accurate and real-time face detection and recognition
Face Library is an open source package for accurate and real-time face detection and recognition

Face Library Face Library is an open source package for accurate and real-time face detection and recognition. The package is built over OpenCV and us

Nov 9, 2022
Nov 29, 2022
Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch
Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch

Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch

Nov 26, 2022
A large-scale face dataset for face parsing, recognition, generation and editing.
A large-scale face dataset for face parsing, recognition, generation and editing.

CelebAMask-HQ [Paper] [Demo] CelebAMask-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA da

Nov 28, 2022
DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition, TPAMI 2021

DVG-Face: Dual Variational Generation for HFR This repo is a PyTorch implementation of DVG-Face: Dual Variational Generation for Heterogeneous Face Re

Nov 28, 2022
YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset
YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset

YOLOv5 ?? is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research int

Nov 25, 2022
img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation
img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation

img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation Figure 1: We estimate the 6DoF rigid transformation of a 3D face (rendered in si

Dec 1, 2022
Code for HLA-Face: Joint High-Low Adaptation for Low Light Face Detection (CVPR21)
Code for HLA-Face: Joint High-Low Adaptation for Low Light Face Detection (CVPR21)

HLA-Face: Joint High-Low Adaptation for Low Light Face Detection The official PyTorch implementation for HLA-Face: Joint High-Low Adaptation for Low L

Nov 25, 2022
AI Face Mesh: This is a simple face mesh detection program based on Artificial intelligence.

AI Face Mesh: This is a simple face mesh detection program based on Artificial Intelligence which made with Python. It's able to detect 468 different

Jan 13, 2022
Official PyTorch implementation and pretrained models of the paper Self-Supervised Classification Network
Official PyTorch implementation and pretrained models of the paper Self-Supervised Classification Network

Self-Classifier: Self-Supervised Classification Network Official PyTorch implementation and pretrained models of the paper Self-Supervised Classificat

Nov 16, 2022
Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)

This is a playground for pytorch beginners, which contains predefined models on popular dataset. Currently we support mnist, svhn cifar10, cifar100 st

Nov 21, 2022
(ImageNet pretrained models) The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"
(ImageNet pretrained models) The official pytorch implemention of the TPAMI paper

Res2Net The official pytorch implemention of the paper "Res2Net: A New Multi-scale Backbone Architecture" Our paper is accepted by IEEE Transactions o

Nov 28, 2022
Implementation of Squeezenet in pytorch, pretrained models on Cifar 10 data to come

Pytorch Squeeznet Pytorch implementation of Squeezenet model as described in https://arxiv.org/abs/1602.07360 on cifar-10 Data. The definition of Sque

Oct 28, 2022
Face Detection & Age Gender & Expression & Recognition
Face Detection & Age Gender & Expression & Recognition

Face Detection & Age Gender & Expression & Recognition

Nov 22, 2022
End-to-end face detection, cropping, norm estimation, and landmark detection in a single onnx model
End-to-end face detection, cropping, norm estimation, and landmark detection in a single onnx model

onnx-facial-lmk-detector End-to-end face detection, cropping, norm estimation, and landmark detection in a single onnx model, model.onnx. Demo You can

Oct 27, 2022
Face and Pose detector that emits MQTT events when a face or human body is detected and not detected.
Face and Pose detector that emits MQTT events when a face or human body is detected and not detected.

Face Detect MQTT Face or Pose detector that emits MQTT events when a face or human body is detected and not detected. I built this as an alternative t

Oct 21, 2022