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 是一个基于浏览器的 AI 集成工具，提供侧边栏界面，以便在浏览网页时与 AI 模型进行交互。它专注于隐私优先的聊天和网页内容分析，允许用户从活动网页中提取信息并进行查询，从而获得上下文感知的回复。 该项目通过本地 AI 集成脱颖而出，支持连接到本地托管的模型或私有 API 端点，无需依赖云服务即可处理数据。它还通过公共共享链接或自托管共享基础设施支持协作式 AI 对话，以实现更好的数据控制。 该系统包含浏览器自动化功能，例如通过将浏览器会话作为一组工具来控制标签页和与页面元素交互。它还通过网页截图和基于 DOM 的内容提取支持视觉处理，以进行全面的页面分析。

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

本项目提供了一个深度残差网络框架和预训练的 PyTorch 模型，专为高精度图像识别而设计。它实现了一种利用跳跃连接（skip connections）的神经网络架构，能够在不发生梯度退化的情况下训练非常深的模型。 该系统专为计算机视觉任务而设计，包括图像分类、目标检测和视觉数据分割。它包含在 ImageNet 上训练的权重，以支持迁移学习和在自定义图像数据集上进行微调。 架构设计侧重于残差学习块、瓶颈层配置和批归一化，以在训练期间保持稳定性。该框架还采用全局平均池化来减少参数并防止过拟合。

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 是一个基于 Web 的计算机视觉应用，旨在识别并从实时视频流中移除人物。该工具使用 TensorFlow.js，作为一个实时背景减除系统，通过分析场景构成将静态背景与移动人物隔离开来。 该项目通过直接在客户端处理摄像头视频流，实现了基于浏览器的计算机视觉。它利用机器学习来区分动态场景元素和背景，从而实现视觉区域内人物的实时移除。

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