Duo Li's repositories
involution
[CVPR 2021] Involution: Inverting the Inherence of Convolution for Visual Recognition, a brand new neural operator
mobilenetv2.pytorch
72.8% MobileNetV2 1.0 model on ImageNet and a spectrum of pre-trained MobileNetV2 models
mobilenetv3.pytorch
74.3% MobileNetV3-Large and 67.2% MobileNetV3-Small model on ImageNet
efficientnetv2.pytorch
PyTorch implementation of EfficientNetV2 family
octconv.pytorch
PyTorch implementation of Octave Convolution with pre-trained Oct-ResNet and Oct-MobileNet models
face-attribute-prediction
Face Attribute Prediction on CelebA benchmark with PyTorch Implementation
ghostnet.pytorch
73.6% GhostNet 1.0x pre-trained model on ImageNet
dgconv.pytorch
PyTorch implementation of Dynamic Grouping Convolution and Groupable ConvNet with pre-trained G-ResNeXt models
regnet.pytorch
PyTorch-style and human-readable RegNet with a spectrum of pre-trained models
lambda.pytorch
PyTorch implementation of Lambda Network and pretrained Lambda-ResNet
mlp-mixer.pytorch
PyTorch implementation of MLP-Mixer
condconv.pytorch
PyTorch implementation of CondConv and MobileNetV2 model
mobilenext.pytorch
Rethinking Bottleneck Structure for Efficient Mobile Network Design
dot-product-attention
A collection of self-attention modules and pre-trained backbones
efficientnet-lite.pytorch
PyTorch implementation of EfficientNet-lite and a spectrum of pre-trained models on ImageNet
deeplearning.ai-CNN
Implementation of course Convolutional Neural Networks created by deeplearning.ai on Coursera
ssd.pytorch
A PyTorch Implementation of Single Shot MultiBox Detector
CS231n-Assignments
Assignments for Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition
attention-transfer
Improving Convolutional Networks via Attention Transfer (ICLR 2017)
axial-deeplab
This is a PyTorch re-implementation of Axial-DeepLab (ECCV 2020 Spotlight)
LearningToCompare_FSL
PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)
weakalign
End-to-end weakly-supervised semantic alignment