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lqcv

Laughing-q Computer Vision Foundation

lunarvim

An IDE layer for Neovim with sane defaults. Completely free and community driven.

metrictrainer

mtcnn_pytorch

MTCNN by pytorch

multiyolov5

joint detection and semantic segmentation, based on ultralytics/yolov5,

myvim

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

Laughing-q's nvim config

nvim.old

Laughing-q's nvim config, modified from theniceboy

projecttemplate-python

Python Project Template

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

ranger

resnest

ResNeSt: Split-Attention Networks

scripts

st

Laughing-q's st, fork from Luke Smith.

test_yolov5

tokyonight.nvim

🏙 A clean, dark Neovim theme written in Lua, with support for lsp, treesitter and lots of plugins. Includes additional themes for Kitty, Alacritty, iTerm and Fish.

toutpuissantged

trackers

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

vim-move

Plugin to move lines and selections up and down

wallpapers

xinye_competition

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+

yolo-q

🔥🔥🔥A inference framework that support multi models of yolo5(torch and tensorrt), yolox(torch and tensorrt), nanodet(tensorrt), yolo-fastestV2(tensorrt) and yolov5-lite(tensorrt).

yolov5-exp

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

yolov5-face

YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931)

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~