Popular GitHub Repositories for M Net

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

Amphion (/?m?fa??n/) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development.

The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility

Samples for ML.NET, an open source and cross-platform machine learning framework for .NET.

The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"

Text recognition (optical character recognition) with deep learning methods, ICCV 2019

A Tensorflow implementation of CapsNet(Capsules Net) in paper Dynamic Routing Between Capsules

.NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.

A C#/.NET library to run LLM (?LLaMA/LLaVA) on your local device efficiently.

TensorFlow API for .NET languages

Foundation Architecture for (M)LLMs

mean Average Precision - This code evaluates the performance of your neural net for object recognition.

AI Multi-Agent Framework in .NET

This is the repo for our new project Highly Accurate Dichotomous Image Segmentation

MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model.

TransmogrifAI (pronounced tr?ns-m?g?r?-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

3D U-Net model for volumetric semantic segmentation written in pytorch