#人脸识别#Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models
翻译 - 预训练的Pytorch人脸检测（MTCNN）和识别（InceptionResnet）模型
Caffe models (including classification, detection and segmentation) and deploy files for famouse networks
Simple Tensorflow implementation of "Squeeze and Excitation Networks" using Cifar10 (ResNeXt, Inception-v4, Inception-resnet-v2)
#计算机科学#Keras/Tensorflow implementation of our paper Grayscale Image Colorization using deep CNN and Inception-ResNet-v2 (https://arxiv.org/abs/1712.03400)
#计算机科学#Inception-v4, Inception - Resnet-v1 and v2 Architectures in Keras
#人脸识别#Webcam face recognition using tensorflow and opencv
#计算机科学#X-ray Images (Chest images) analysis and anomaly detection using Transfer learning with inception v2
Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification
我的笔记和Demo，包含分类，检测、分割、知识蒸馏。
A step by step guide on how to use tensorflow serving to serve a tensorflow model.
#计算机科学#Naruto Hand Gesture Recognition with OpenCV and Transfer Learning
#计算机科学#Non-parallel voice conversion called ICRCycleGAN-VC based on CycleGAN and Inception-resNet module by Afiuny
#人脸识别#Attendance Monitoring System that has a tracker, face detection, face recognition and database connectivity all integrated together. The tracker is based on Strongsort, yolov8 is used for face detecti...
This is the companion repository for our paper iSPLInception: Redefining the State-of-the-Art for Human Activity Recognition which will be published in IEEE Access - 2021.
Keras and tensorflow transfer learning, starting from the pre-trained inception_resnetV2 model
Developed a deep novel coupled profile to frontal face recognition network incorporating pose as an auxiliary information via attention mechanism (i.e., implemented a pose attention module).
Image Style Recognition using Transfer Learning with Pre-trained ResNet
Attendance Monitoring
#计算机科学#Chainer implementation of the paper "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning" (https://arxiv.org/abs/1602.07261)