wangerniu's repositories
FabricDefectDetection
AIFT2019-Real-time fabric defect segmentation based on convolutional neural network
Attention-Gated-Networks
Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation
AutoGluon-Tutorial-CVPR2020
Hands-on Tutorial on Automated Deep Learning
BASNet
Code for CVPR 2019 paper. BASNet: Boundary-Aware Salient Object Detection
best-of-ml-python
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
chatgpt-web
用 Express 和 Vue3 搭建的 ChatGPT 演示网页
Class-balanced-loss-pytorch
Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"
cv-arxiv-daily
🎓Automatically Update CV Papers Daily using Github Actions (Update Every 12th hours)
IMC-2023
7th place solution for the Image Matching Challenge 2023.
keras-flask-deploy-webapp
:smiley_cat: Pretty & simple image classifier app template. Deploy your own trained model or pre-trained model (VGG, ResNet, Densenet) to a web app using Flask in 10 minutes.
nanopi-openwrt
Openwrt for Nanopi R1S R2S 固件编译
OpenWrt-Rpi
Raspberry Pi & NanoPi R2S & G-Dock & x86 OpenWrt Compile Project. (Based on Github Action / Daily Update)
optuna
A hyperparameter optimization framework
OrienterNet
Source Code for Paper "OrienterNet Visual Localization in 2D Public Maps with Neural Matching"
pytorch-image-models
PyTorch image models, scripts, pretrained weights -- (SE)ResNet/ResNeXT, DPN, EfficientNet, MixNet, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet, and more
R2S-OpenWrt
OpenWrt for Nanopi R2S
satellite-thermal-geo-localization
Official repository for "Long-range UAV Thermal Geo-localization with Satellite Imagery"
streamlit-example
Example Streamlit app that you can fork to test out share.streamlit.io
Test
None
TFSegmentation
RTSeg: Real-time Semantic Segmentation Comparative Study
wangerniuniu.github.io
Play and chat smarter with BetterChatGPT - an amazing open-source web app with a better UI for exploring OpenAI's ChatGPT API!
YOLOv3-model-pruning
对 YOLOv3 做模型剪枝（network slimming），对于 oxford hand 数据集（因项目需要），模型剪枝后的参数量减少 80%，Infer 的速度达到原来 2 倍，mAP 基本不变