许佳炜's repositories
WiGr
WiFi-based Cross-Domain Gesture Recognition via Modified Prototypical Networks
WiSR
WiSR: Wireless Domain Generalization Based on Style Randomization
langchain-ChatGLM
langchain-ChatGLM, local knowledge based ChatGLM with langchain ｜ 基于本地知识库的 ChatGLM 问答
HPM
[CVPR'23] Hard Patches Mining for Masked Image Modeling
mae
PyTorch implementation of MAE https//arxiv.org/abs/2111.06377
stargan-v2
StarGAN v2 - Official PyTorch Implementation (CVPR 2020)
segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
NER-Pytorch-Chinese
Implemention of NER model on chinese dataset.
L2G
The PyTorch Code for our CVPR 2022 paper "L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation"
wumei-smart
物美智能(wumei-smart)
irn
Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations, CVPR 2019 (Oral)
EfficientFormer
EfficientFormerV2 & EfficientFormer(NeurIPs 2022)
Linear-Algebra-With-Python
Lecture Notes for Linear Algebra Featuring Python. This series of lecture notes will walk you through all the must-know concepts that set the foundation of data science or advanced quantitative skillsets. Suitable for statistician/econometrician, quantitative analysts, data scientists and etc. to quickly refresh the linear algebra with the assistan
efficientdet-pytorch
这是一个efficientdet-pytorch的源码，可以用于训练自己的模型。
deep-learning-for-image-processing
deep learning for image processing including classification and object-detection etc.
DynamicViT
[NeurIPS 2021] DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
two-branch-plant-disease
Source code for the paper "Color-aware two-branch DCNN for efficient plant disease classification".
Python_CsiNet
Python code for "Deep Learning for Massive MIMO CSI Feedback"
vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch