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lightkg

基于Pytorch和torchtext的知识图谱深度学习框架，包含知识表示学习、实体识别与链接、实体关系抽取、事件检测与抽取、知识存储与查询、知识推理六大功能模块，已实现了命名实体识别、关系抽取、事件抽取、表示学习等功能。框架功能丰富，开箱可用，极易上手！基本都是学习他人实现然后自己修改融合到框架中，没有细致调参，且有不少Bug～

lighttrack

Code for paper "LightTrack: A Generic Framework for Online Top-Down Human Pose Tracking"

likely

Likely Kernel Library

lip2wav

This is the repository containing codes for our CVPR, 2020 paper titled "Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis"

lip_ssl

Code repository for Self-supervised Structure-sensitive Learning, CVPR'17

lipreading_using_temporal_convolutional_networks

ICASSP'21 Towards Practical Lipreading with Distilled and Efficient Models; ICASSP'20 Lipreading using Temporal Convolutional Networks

lite.ai

Lite.AI 🚀🚀🌟 is a user friendly C++ lib of awesome AI models. such as YOLOX🔥, YoloR🔥, YoloV5, YoloV4, DeepLabV3, ArcFace, CosFace, RetinaFace, SSD, etc.

liveness-detection-introduction

活体检测方法调研，introduction to liveness detection

livespeechportraits

Live Speech Portraits: Real-Time Photorealistic Talking-Head Animation (SIGGRAPH Asia 2021)

look-into-person

This repository is to do Human Parsing with SegNet.

looking-to-listen-at-the-cocktail-party

Executable code based on Google articles

lstm-human-activity-recognition

Human activity recognition using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six categories (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING).

lstmae

PyTorch implementation of LstmAE (Outlier Detection for Multidimensional Time Series Using Deep Neural Networks)

lstr

This is an official repository of End-to-end Lane Shape Prediction with Transformers.

ltnet

Implement of LTNet in "Facial expression recognition with inconsistent datasets", ECCV 2018

m-ce2p

m2det

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

mahotas

Computer Vision in Python

make-sense

Free to use online tool for labelling photos. https://makesense.ai

masktextspotter

A PyTorch implementation of Mask TextSpotter

matio

MATLAB MAT File I/O Library

medical-data

medical-image-classification-using-deep-learning

Tumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.

medicalassistanservice

医疗数据爬虫、简单病症分析及医疗资源推荐

medicalnamedentityrecognition

Medical Named Entity Recognition implement using bi-directional lstm and crf model with char embedding.CCKS2017中文电子病例命名实体识别项目,主要实现使用了基于字向量的四层双向LSTM与CRF模型的网络.该项目提供了原始训练数据样本(一般醒目,出院情况,病史情况,病史特点,诊疗经过)与转换版本,训练脚本,预训练模型,可用于序列标注研究.把玩和PK使用.

mexopencv

Collection and a development kit of matlab mex functions for OpenCV library

micro-expression-with-deep-learning

Experimentation of deep learning on the subjects of micro-expression spotting and recognition.

micro_expression_res3d_casme2_tensorflow

A res_3D based detection system for micro expression recognition

microexpression-detection

Micro-Facial Expression Detection using 3D Convolutional Neural Networks

mmal-net

This is a PyTorch implementation of the paper "Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization (MMAL-Net)" (Fan Zhang, Meng Li, Guisheng Zhai, Yizhao Liu).