Laughing's repositories
yolov5_annotations
annotations of yolov5-5.0
.tmux
🇫🇷 Oh my tmux! My self-contained, pretty & versatile tmux configuration made with ❤️
antovim
Super-simple vim plugin for cycling through antonyms/words related to word under cursor
Cpp_Primer_Practice
搞定C++:punch:。C++ Primer 中文版第5版学习仓库，包括笔记和课后练习答案。
DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
Detic
Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".
dmenu
dmenu setup for LARBS
dwmblocks
My status bar: my build of the modular dwmblocks
facexlib
FaceXlib aims at providing ready-to-use face-related functions based on current STOA open-source methods.
fake-chinese-license-plate
本项目旨在自动生成虚拟的**大陆地区标准车牌图像，并提供简单的数据增强手段，来增强视觉感官上的逼真程度。
GFPGAN
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
kapao
KAPAO is an efficient multi-person human pose estimation model that detects keypoints and poses as objects and fuses the detections to predict human poses.
LeaderF
An efficient fuzzy finder that helps to locate files, buffers, mrus, gtags, etc. on the fly for both vim and neovim.
LunarVim
An IDE layer for Neovim with sane defaults. Completely free and community driven.
nanodet
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
NvChad
An attempt to make neovim cli as functional as an IDE while being very beautiful, blazing fast startuptime ~ 0.05 secs
nvim.old
Laughing-q's nvim config, modified from theniceboy
pytorch-image-models
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more
ResNeSt
ResNeSt: Split-Attention Networks
undotree
The undo history visualizer for VIM
vim-matchup
vim match-up: even better % :facepunch: navigate and highlight matching words :facepunch: modern matchit and matchparen
Yolo-FastestV2
:zap: Based on Yolo's low-power, ultra-lightweight universal target detection algorithm, the parameter is only 250k, and the speed of the smart phone mobile terminal can reach ~300fps+
YOLOv5-Lite
🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.9M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~
YOLOX
YOLOX is a high-performance anchor-free YOLO. Exceeding yolov3~v5 with ONNX, TensorRT, NCNN, and Openvino supported.