facenet-pytorch | Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models | Computer Vision library

by timesler Python Version: 2.6.0 License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | facenet-pytorch Summary

facenet-pytorch is a Python library typically used in Artificial Intelligence, Computer Vision, Pytorch, Tensorflow applications. facenet-pytorch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install facenet-pytorch' or download it from GitHub, PyPI.

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

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facenet-pytorch has a medium active ecosystem.

It has 3538 star(s) with 798 fork(s). There are 46 watchers for this library.

There were 1 major release(s) in the last 12 months.

There are 59 open issues and 104 have been closed. On average issues are closed in 225 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of facenet-pytorch is 2.6.0

Quality

facenet-pytorch has 0 bugs and 0 code smells.

Security

facenet-pytorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

facenet-pytorch code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

facenet-pytorch is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

facenet-pytorch releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

facenet-pytorch saves you 601 person hours of effort in developing the same functionality from scratch.

It has 1401 lines of code, 74 functions and 10 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed facenet-pytorch and discovered the below as its top functions. This is intended to give you an instant insight into facenet-pytorch implemented functionality, and help decide if they suit your requirements.

Convert tensorflow to pytorch
Compares two tensorflow matrices
Compares two model outputs
Load weights from mdl
Load mixed7 weights
Load tf TensorNorm
Loads a batchNorm layer
Load tf2d layer
Detect faces in the given image
Extract an image from a box
Extracts the faces of the given image
Detects the face of the image
Detect the face of the image
Performs a batch of nms averaging
Compute the NMS algorithm
Detect the face of input images
Load pretrained weights
Download a URL to a file
Return the path to the torch library
Returns the path to the torch directory
Loads the TF block 35
Loads mixed6 weights
Loadtf blocks of 8 bits

Get all kandi verified functions for this library.

facenet-pytorch Key Features

No Key Features are available at this moment for facenet-pytorch.

facenet-pytorch Examples and Code Snippets

训练步骤

Python

Lines of Code : 8

License : Permissive (MIT)

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|-datasets
    |-people0
        |-123.jpg
        |-234.jpg
    |-people1
        |-345.jpg
        |-456.jpg
    |-...

预测步骤,b、使用自己训练的权重

Python

Lines of Code : 8

License : Permissive (MIT)

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_defaults = {
    "model_path"    : "model_data/facenet_mobilenet.pth",
    "input_shape"   : (160, 160, 3),
    "backbone"      : "mobilenet",
    "cuda"          : True,
}

img\1_001.jpg
img\1_002.jpg

预测步骤,a、使用预训练权重

Python

Lines of Code : 4

License : Permissive (MIT)

Copy

img\1_001.jpg
img\1_002.jpg

Community Discussions

Trending Discussions on facenet-pytorch

Unexpected error when loading the model: problem in predictor - ModuleNotFoundError: No module named 'torchvision'

ValueError: sampler option is mutually exclusive with shuffle pytorch

QUESTION

Unexpected error when loading the model: problem in predictor - ModuleNotFoundError: No module named 'torchvision'

Asked 2020-May-28 at 18:00

I've been trying to deploy my model to the AI platform for Prediction through the console on my vm instance, but I've gotten the error "(gcloud.beta.ai-platform.versions.create) Create Version failed. Bad model detected with error: "Failed to load model: Unexpected error when loading the model: problem in predictor - ModuleNotFoundError: No module named 'torchvision' (Error code: 0)"

I need to include both torch and torchvision. I followed the steps in this question Cannot deploy trained model to Google Cloud Ai-Platform with custom prediction routine: Model requires more memory than allowed, but I couldn't fetch the files pointed to by user gogasca. I tried downloading this .whl file from Pytorch website and uploading it to my cloud storage but got the same error that there is no module torchvision, even though this version is supposed to include both torch and torchvision. Also tried using Cloud AI compatible packages here, but they don't include torchvision.

I tried pointing to two separate .whl files for torch and torchvision in the --package-uris arguments, those point to files in my cloud storage, but then I got the error that the memory capacity was exceeded. This is strange, because collectively their size is around 130Mb. An example of my command that resulted in absence of torchvision looked like this:

...

ANSWER

Answered 2020-May-23 at 15:19

The solution was to place the following packages in thsetup.py file for the custom prediction code:

Source https://stackoverflow.com/questions/61933879

QUESTION

ValueError: sampler option is mutually exclusive with shuffle pytorch

Asked 2020-May-13 at 13:03

i'm working on face recognition project using pytorch and mtcnn and after trained my training dataset , now i want to make prediction on test data set

this my trained code

...

ANSWER

Answered 2020-Apr-04 at 19:49

I'm not sure what format you test data is in but to select a sample randomly from your dataset, you can use random.choice from the module random.

Source https://stackoverflow.com/questions/61033726

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install facenet-pytorch

In python, import facenet-pytorch and instantiate models:. See help(MTCNN) and help(InceptionResnetV1) for usage and implementation details.
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
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()
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))

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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