Awesome GitHub RepositoriesCustom Data Transform Extensions

MMSegmentation is an open-source semantic segmentation toolbox built on PyTorch that provides a modular, configurable framework for building, training, evaluating, and deploying segmentation models. At its core, it offers a config-driven pipeline that assembles training, evaluation, and inference workflows by parsing hierarchical configuration files, with a modular component registry that enables plug-and-play composition of neural network modules, optimizers, datasets, and metrics. The framework supports the full model lifecycle through a unified runner interface that controls training, testi

OpenNMT-py is a PyTorch neural machine translation framework used for training and deploying neural machine translation and large language models. It functions as a distributed model training system, an inference engine, and a toolkit for fine-tuning large language models. The framework distinguishes itself with a dedicated toolkit for adapting large language models through low-rank adaptation, quantization, and instruction tuning. It also includes a neural machine translation server that allows trained models to be hosted and exposed via REST API endpoints. The project covers a broad range

mmcv is a foundation library for computer vision based on PyTorch. It provides a comprehensive system for constructing convolutional neural networks, a toolkit for image and video preprocessing, and a collection of high-performance deep learning vision operators. The project is distinguished by its hardware-accelerated kernels for complex operations such as deformable convolutions and region pooling. It features a configuration-driven framework that allows for the dynamic instantiation of network layers and the registration of custom modules without modifying code. The library covers a broad

Anomalib is a PyTorch-based library for visual anomaly detection, offering a modular framework, a comprehensive model zoo, and a benchmarking suite designed for industrial defect detection. It provides a wide range of algorithms—including generative, discriminative, teacher-student, and vision-language approaches—that support unsupervised, few-shot, and zero-shot settings. The library enables deployment through model export to ONNX and OpenVINO for edge devices, and includes a no-code web application for training and inference. It also features a command-line interface for orchestrating multi

mmaction2 este un set de instrumente PyTorch pentru înțelegerea video, conceput pentru antrenarea și evaluarea modelelor de deep learning. Servește ca un framework pentru recunoașterea acțiunilor, localizarea temporală și detectarea acțiunilor spatio-temporale, oferind instrumente specializate atât pentru analiza video bazată pe pixeli, cât și pentru recunoașterea acțiunilor bazată pe schelet. Proiectul se distinge printr-o arhitectură modulară care dispune de descoperirea componentelor bazată pe registru și asamblarea ierarhică a modelelor bazată pe configurație. Suportă fuziunea caracteristicilor multi-modale, integrând cadre RGB, flux optic și audio, și include capabilități pentru recuperarea clipurilor video din text și predicția video zero-shot. În linii mari, framework-ul acoperă ingineria seturilor de date video, inclusiv standardizarea adnotărilor și eșantionarea cadrelor, precum și antrenarea și evaluarea cuprinzătoare a modelelor. Oferă utilitare pentru antrenarea distribuită, distilarea cunoștințelor și optimizarea inferenței prin reparametrizarea modelului. Codul sursă suportă exportul modelelor ONNX și containerizarea mediului pentru implementarea pe diferite noduri de calcul.

This repository is a collection of reference implementations, templates, and sample galleries for building and integrating machine learning models within the .NET ecosystem. It provides a set of practical demonstrations for implementing machine learning workflows using the ML.NET framework. The project emphasizes the integration of pre-trained models via the Open Neural Network Exchange format, allowing the execution of external machine learning logic within managed applications. It includes specific examples for loading and executing these standardized models to ensure cross-platform compati

This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen