#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
#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...
#计算机科学#Non-parallel voice conversion called ICRCycleGAN-VC based on CycleGAN and Inception-resNet module by Afiuny
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)