bigpandacpu Goto Github PK

ai_sudoku

GUI based Smart Sudoku Solver that tries to extract a sudoku puzzle from a photo and solve it

awesome-ai-list-guide

The guide of awesome list about AI

awesome-transformers-in-medical-imaging

A collection of resources on applications of Transformers in Medical Imaging.

cleanmywechat

自动删除 PC 端微信缓存数据，包括从所有聊天中自动下载的大量文件、视频、图片等数据内容，解放你的空间。

cmake-examples

Useful CMake Examples

code-samples

Source code examples from the Parallel Forall Blog

cpp-memory-management

侯捷C++内存管理专题笔记

cpp_houjie

侯捷C++课程PPT及代码,动手学起来

ctspine1k

A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography

cuda_by_example

The CMake version of cuda_by_example

darknet

AlexeyAB-DarkNet源码解析

dataset

医学影像数据集列表 『An Index for Medical Imaging Datasets』

deep_learning_coding

Deep learning from scratch.

flappybird_qlearning

github-chinese-top-charts

:cn: GitHub中文排行榜，各语言分离设置「软件 / 资料」榜单，精准定位中文好项目。各取所需，互不干扰，高效学习。

gitlearn

how-to-learn-deep-learning-framework

how to learn PyTorch and OneFlow

image-processing-algorithm

paper implement

medical-conversions

dicom图像转换工具

newcrnn

此项目提供了NewCRNN的pytorch训练脚本及将模型文件从pytorch转keras，keras装darknet格式的转换脚本，另外此模型是在chinseOcr的ocr模型基础上进行整理的

nndetection

nnDetection is a self-configuring framework for 3D (volumetric) medical object detection which can be applied to new data sets without manual intervention. It includes guides for 12 data sets that were used to develop and evaluate the performance of the proposed method.

pcl-learning

🔥PCL（Point Cloud Library）点云库学习记录

pdfviewer

This project is used to manage SACViewer source

sam-med2d

Official implementation of SAM-Med2D

sam-med3d

SAM-Med3D: An Efficient General-purpose Promptable Segmentation Model for 3D Volumetric Medical Image

segyreadwrite

spinesurgicalplanning

stampdetectocr

针对某银行的事后监督系统，需要的识别票据的主键、流水号及附件章等。基于opencv的dnn模块实现的，yolov3目标检测+改进的CRNN模型（CNN+LSTM+FC+Beam_search -> CNN+FC）的ocr模型

tensorrt_pro

C++ library based on tensorrt integration

toys

Toy models, experiments and random notes on machine learning and deep learning.