insightface | State-of-the-art 2D and 3D Face Analysis Project | Computer Vision library
kandi X-RAY | insightface Summary
kandi X-RAY | insightface Summary
InsightFace is an open source 2D&3D deep face analysis toolbox, mainly based on PyTorch and MXNet. Please check our website for detail. The master branch works with PyTorch 1.6+ and/or MXNet=1.6-1.8, with Python 3.x. InsightFace efficiently implements a rich variety of state of the art algorithms of face recognition, face detection and face alignment, which optimized for both training and deployment.
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Top functions reviewed by kandi - BETA
- Get resnet conv layer .
- Gets the convolution layer .
- Assign anchors to the image .
- Get output for convolution .
- get the SSH convolution layer
- get crop image2
- Main function .
- Train network .
- Parse command line arguments
- Test for bad case .
insightface Key Features
insightface Examples and Code Snippets
faces_emore/
train.idx
train.rec
property
lfw.bin
cfp_fp.bin
agedb_30.bin
python tools/mx_recordio_2_ofrecord_shuffled_npart.py --data_dir datasets/faces_emore --output_filepath faces_emore/ofrecord/train --part_
models
├── dlib-19.17
│ ├── models
│ ├── dlib_face_recognition_resnet_model_v1.dat
│ └── shape_predictor_5_face_landmarks.dat
├── facenet
│ ├── models
pip install retina-face
from retinaface import RetinaFace
resp = RetinaFace.detect_faces("img1.jpg")
{
"face_1": {
"score": 0.9993440508842468,
"facial_area": [155, 81, 434, 443],
"landmarks": {
"right_eye": [2
import sys
import os
import argparse
import onnx
import json
import mxnet as mx
from onnx import helper
from onnx import TensorProto
from onnx import numpy_helper
import onnxruntime
import cv2
print('mxnet version:', mx.__version__)
print('onnx vers
import datetime
import numpy as np
import os
import os.path as osp
import glob
import cv2
import insightface
assert insightface.__version__>='0.4'
def detect_person(img, detector):
bboxes, kpss = detector.detect(img)
bboxes = np.round(b
import datetime
import numpy as np
import os
import os.path as osp
import glob
import cv2
import insightface
from insightface.app import FaceAnalysis
from insightface.data import get_image as ins_get_image
assert insightface.__version__>='0.7'
#le = LabelEncoder()
#labels = le.fit_transform(data["names"])
#num_classes = len(np.unique(labels))
#labels = labels.reshape(-1, 1)
#one_hot_encoder = OneHotEncoder(categorical_features = [0])
#labels = one_hot_encoder.fit_transform(label
face_imgs_resized = np.ones((128,224,224,3))
face_imgs_resized = np.array(face_imgs_resized)
face_imgs_resized = np.rollaxis(face_imgs_resized, 3, 1)
data = mx.nd.array(face_imgs_resized)
db = mx.io.DataBatch(data=mx.nd.split_v2(data, 4))
Community Discussions
Trending Discussions on insightface
QUESTION
I'm using the Insightface library from Pypi (https://pypi.org/project/insightface/), the source code is here: https://github.com/deepinsight/insightface/blob/master/python-package/insightface/model_zoo/scrfd.py.
When I run it on my GPU there is a severe memory leak of the CPU's RAM, over 40 GB until I stopped it (not the GPU memory).
here is my script:
...ANSWER
Answered 2021-Jul-15 at 19:14I managed to solve it with the following setup:
- Ubuntu-20.04
- Python-3.8
- Nvidia-470
- Cuda-11.3
- Cudnn-8
- mxnet==1.8.0.post0
- onnx==1.9.0
- onnxruntime-gpu==1.8.1
- insightface==0.4
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