Zhong Yaoyao's repositories
Face-Transformer
Face Transformer for Recognition
Unequal-Training-for-Deep-Face-Recognition-with-Long-Tailed-Noisy-Data
Code for CVPR2019 paper《Unequal Training for Deep Face Recognition with Long Tailed Noisy Data》
Adversarial_MTER
Code for ICCV2019 paper《Adversarial Learning with Margin-based Triplet Embedding Regularization》
Deep-Difference-Analysis-in-Similar-looking-face-recognition
Code of ICPR 2018 paper《Deep Difference Analysis in Similar-looking Face recognition》
interpretable-face
Code of AFGR 2019 paper《Exploring Features and Attributes in Deep Face Recognition Using Visualization Techniques》
adversarial_image_defenses
Countering Adversarial Image using Input Transformations.
insightface
Face Recognition Project on MXNet
Awesome-SLU-Survey
Tracking the progress in SLU (resources, code, and new frontiers etc.)
CurricularFace
CurricularFace(CVPR2020)
face-parsing.PyTorch
Using modified BiSeNet for face parsing in PyTorch
face.evoLVe
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥
InsightFace_Pytorch
Pytorch0.4.1 codes for InsightFace
jittorface
Codes of some face models inplemented by Jittor.
lit-llama
Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.
Meta-Learning-Papers
Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning
MobileNet-Pytorch
Implementation of MobileNet V1, V2, V3
pytorch-cifar100
Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet)
SENet.mxnet
:fire::fire: A MXNet implementation of Squeeze-and-Excitation Networks (SE-ResNext, SE-Resnet, SE-Inception-v4 and SE-Inception-Resnet-v2) :fire::fire:
sphereface
Implementation for <SphereFace: Deep Hypersphere Embedding for Face Recognition> in CVPR'17.
video_features
Extract video features from raw videos using multiple GPUs. We support RAFT and PWC flow frames as well as I3D, R(2+1)D, VGGish, ResNet features.
vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch