Awesome GitHub RepositoriesImage Classification Models

Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification. By utilizing a modular architecture, the platform allows users to swap model components to balance inference speed and accuracy requirements for diverse applications. The framework distinguishes itself through its support for real-time processing and flexible deployment. It in

YOLOv5 is a comprehensive computer vision framework designed for end-to-end deep learning, specializing in real-time object detection, image classification, and instance segmentation. It provides a unified toolkit that manages the entire lifecycle of a model, from initial dataset configuration and hyperparameter tuning to high-speed inference and deployment. The framework utilizes a modular neural architecture, allowing users to swap backbone and head components to tailor models for specific visual tasks. What distinguishes this project is its focus on production-ready deployment and model ef

This project is a library of pretrained computer vision architectures and backbones for image classification and feature extraction. It serves as a comprehensive model zoo and collection of standardized image encoders, including ResNet, Vision Transformers, and EfficientNet, for use in visual analysis and as backbones for object detection and image segmentation. The library provides a framework for distributed training and evaluation of image models using advanced data augmentation and optimization scripts. It includes a dedicated toolset for converting trained PyTorch vision models into the

This repository serves as a comprehensive collection of reference implementations for the PyTorch machine learning library. It provides practical examples for building, training, and deploying deep learning models, functioning as a toolkit for developers to explore neural network architectures and training workflows. The project distinguishes itself by offering concrete demonstrations of complex machine learning operations, ranging from computer vision tasks like object detection and depth estimation to the training of large-scale transformer models. These examples illustrate how to implement

This project is a deep learning framework designed for constructing, training, and deploying neural networks across diverse hardware environments. It functions as a high-performance tensor computation library that provides both imperative and symbolic programming interfaces, allowing developers to balance flexible, step-by-step model building with the efficiency of compiled computation graphs. The framework distinguishes itself through a hybrid execution engine that integrates declarative graph compilation with imperative runtime logic. It supports scalable, distributed training across multip

TensorFlow.js is a JavaScript machine learning library used for training and deploying models in web browsers and server-side environments. It functions as a browser-based model trainer, a WebAssembly inference engine, and a WebGPU accelerated tensor library for low-level linear algebra. The project also includes a model converter to transform Python-based models into optimized formats for JavaScript execution. The library distinguishes itself through a pluggable backend architecture that allows mathematical operations to be executed via CPU, WebGL, or WebGPU. It supports the conversion of Py

ai-edu is a comprehensive AI education curriculum and machine learning courseware collection. It provides theoretical tutorials, deep learning lab exercises, and project blueprints designed to teach artificial intelligence fundamentals through a combination of study and practical implementation. The project focuses on a learning-by-doing approach, guiding users from Python programming and neural network basics to advanced topics. It includes specialized instructional content on distributed AI training, MLOps educational guides for model quantization and pruning, and detailed frameworks for im

AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end pipeline from data preprocessing to high-accuracy model training and validation. It functions as an automated model trainer for tabular, image, text, and time series data, as well as a tool for time series forecasting and foundation model finetuning. The project is distinguished by its ability to jointly process and fuse different data types, allowing for the construction of multimodal neural networks that integrate images, text, and structured tables. It supports zero-shot inferenc

NSFW detection on the client-side via TensorFlow.js

ImageAI is a Python computer vision library providing a suite of tools for image classification, object detection, and video analytics. It functions as an integrated framework for locating and labeling objects in static images and video streams, utilizing deep learning models for identification and categorization. The project includes a model training toolkit that allows for the creation of custom classifiers and detectors through scratch training or transfer learning. It features a GPU-accelerated inference engine to increase processing speed for vision tasks and includes specialized utiliti

Page Assist este un instrument de integrare AI pentru browser care oferă o interfață de tip sidebar pentru interacțiunea cu modele AI în timp ce navigați pe web. Se concentrează pe chat-ul axat pe confidențialitate și analiza conținutului web, permițând utilizatorilor să extragă și să interogheze informații din paginile web active pentru a primi răspunsuri conștiente de context. Proiectul se distinge prin integrarea AI locală, permițând conexiuni la modele găzduite local sau endpoint-uri API private pentru a procesa datele fără a depinde de servicii cloud. De asemenea, suportă conversații AI colaborative prin link-uri de partajare publică sau infrastructură de partajare auto-găzduită pentru un control mai bun al datelor. Sistemul include capabilități pentru automatizarea browserului, cum ar fi controlul tab-urilor și interacțiunea cu elementele paginii prin expunerea sesiunii de browser ca un set de instrumente. Suportă, de asemenea, procesarea vizuală prin capturi de ecran ale paginilor web și extragerea conținutului bazată pe DOM pentru o analiză cuprinzătoare a paginii.

tensorrtx is a computer vision inference engine and model implementation library designed for graphics processor acceleration. It provides a framework for optimizing deep learning models through a GPU inference optimizer, a deep learning model converter for transforming weights from frameworks like TensorFlow and PyTorch, and a custom plugin library to implement operations not natively supported by the TensorRT API. The project distinguishes itself through a comprehensive collection of pre-defined network implementations, ranging from various YOLO versions and DETR transformers for object det

This project is a collection of deep learning tools for image classification and audio tagging, providing a repository of pre-trained model weights and architectures. It serves as a Keras model zoo that enables the immediate use of established neural networks for inference and transfer learning. The library includes a music tagging framework that classifies audio recordings using convolutional recurrent neural networks and mel-spectrograms. For visual data, it provides implementations of architectures such as ResNet, VGG, and Xception, alongside a repository of weights trained on large datase

Acest proiect oferă un framework de rețele reziduale adânci și modele PyTorch pre-antrenate, concepute pentru recunoașterea imaginilor cu precizie ridicată. Implementează o arhitectură de rețea neuronală care utilizează conexiuni de tip skip pentru a permite antrenarea unor modele foarte adânci fără degradarea gradientului. Sistemul este conceput pentru sarcini de viziune computerizată, inclusiv clasificarea imaginilor, detectarea obiectelor și segmentarea datelor vizuale. Include ponderi antrenate pe ImageNet pentru a susține transfer learning-ul și ajustarea fină (fine-tuning) a modelelor pe seturi de date de imagini personalizate. Designul arhitectural se concentrează pe blocuri de învățare reziduală, configurații de straturi bottleneck și normalizarea batch-urilor pentru a menține stabilitatea în timpul antrenamentului. Framework-ul utilizează, de asemenea, global average pooling pentru a reduce numărul de parametri și a preveni overfitting-ul.

Code release for ConvNeXt model

CodeCompanion is a Neovim plugin that brings large language model capabilities directly into the editor, enabling turn-based conversations with AI models in a dedicated chat buffer. It provides a comprehensive interface for interacting with LLMs, supporting multiple providers through a flexible adapter system that can route requests to various hosted or local language model services. The plugin distinguishes itself through its extensive context-sharing capabilities, allowing users to send buffer contents, visual selections, git diffs, LSP diagnostics, terminal output, quickfix lists, and view

Kalidokit is a web-based motion capture tool that transforms real-time webcam video into 3D character animation data. It functions as a blendshape and kinematics calculator, converting facial, hand, and body tracking data from Mediapipe and TensorFlow.js into blendshape weights and euler rotations for driving digital puppets and avatars. The tool solves face landmarks to derive head rotation, eye blinks, mouth shapes, and brow values for rigging, while hand landmarks are converted into finger joint rotations and body keypoints into per-joint euler rotations for full-body animation. It include

Captum is an open-source library for explaining model predictions by attributing them to input features, neurons, and layers using gradient-based and perturbation-based methods. It provides a modular framework for implementing, evaluating, and combining a range of explanation techniques, including gradient-based attribution, perturbation-based analysis, game-theoretic Shapley value approximation, and surrogate model explanations, with support for parallelization and noise stabilization. The library distinguishes itself through its breadth of attribution methods and its support for advanced in

Real-Time-Person-Removal este o aplicație de computer vision bazată pe web, concepută pentru a identifica și elimina figurile umane din fluxurile video live. Folosind TensorFlow.js, instrumentul funcționează ca un sistem de scădere a fundalului în timp real care analizează compoziția scenei pentru a izola fundalurile statice de persoanele în mișcare. Proiectul permite computer vision bazat pe browser prin procesarea fluxurilor video de la webcam direct în client. Utilizează machine learning pentru a diferenția între elementele dinamice ale scenei și fundal, permițând eliminarea în timp real a persoanelor din câmpul vizual.

This repository is a collection of YAML automations and scripts for Home Assistant that coordinate devices and services into context‑aware home routines. Its design centers on combining multiple sensor inputs—motion, bed presence, garage door state—into a single occupancy signal that gates automations, reducing false triggers. The collection delivers actionable mobile notifications with script‑bound buttons for snoozing or escalating alerts, and integrates an external vision service to classify camera snapshots so package deliveries are detected and announced. What distinguishes this configur