Awesome GitHub RepositoriesVideo Stream Detections

This is a real-time object detection framework built on the YOLOv3 architecture, implemented in PyTorch. It provides a complete pipeline for identifying and localizing objects in images and video using a single neural network pass, combining a Darknet-53 backbone with multi-scale feature pyramids and anchor-based bounding box prediction. The framework extends beyond basic detection to include instance segmentation, human pose estimation, and multi-object tracking across video frames. It offers a model export toolkit that converts trained models through ONNX to CoreML, TensorFlow Lite, and Ten

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

该项目是一个实现 Faster R-CNN 架构的 PyTorch 目标检测框架。它作为一种视觉模型，用于预测图像和实时视频流中多个目标周围的精确边界框。 该系统针对多 GPU 训练进行了优化，以减少模型收敛所需的时间。它利用 GPU 加速设计来处理复杂检测网络的训练和推理。 该框架涵盖了完整的目标检测生命周期，包括针对静态图像和实时视频流的自定义网络训练和推理。它包括使用标准化数据集进行模型性能验证的功能，以及诸如基于长宽比的分组和分布式工作负载等训练优化。

This project is an object detection framework implementing the YOLOv3 architecture using Keras and TensorFlow. It functions as a deep learning vision model and computer vision toolset designed to locate and classify multiple entities within images and video streams using bounding boxes. The system includes a multi-GPU inference engine to distribute computational loads across several graphics processing units. It also provides a pipeline for creating custom object detectors by retraining pre-trained weights on annotated datasets to recognize user-defined object classes. The framework covers m

PaddleX is a PaddlePaddle-based framework for building, deploying, and fine-tuning AI model pipelines, with pre-built support for computer vision, OCR, document analysis, and time series tasks. It offers a toolkit of ready-to-use pipelines for image classification, object detection, segmentation, and pose estimation, alongside an end-to-end OCR document analysis pipeline that extracts text, tables, formulas, and layout information. The platform also includes a dedicated time series forecasting pipeline for analyzing historical data to detect anomalies, classify patterns, and predict future val

yolotf is an object detection framework that provides tools for converting Darknet model configurations and weights into TensorFlow graphs. It includes a TensorFlow model trainer for training new detection models or fine-tuning existing weights using custom datasets. The project features a mobile model exporter that serializes graph definitions and metadata into protobuf files for deployment on mobile devices. The framework supports object detection inference on images and video to identify objects and export bounding box coordinates. It manages model state through weight-mapping translation

This is the official documentation repository for Raspberry Pi hardware and software. It covers the complete range of Raspberry Pi single-board computers, the RP-series microcontrollers, and the Raspberry Pi operating system. The documentation provides reference material for setting up devices, configuring hardware, and using the system for tasks including AI inference, camera and video capture, embedded development, and remote access. The documentation covers the full boot chain from the GPU firmware and EEPROM bootloader through to kernel loading, with detailed guidance on boot configuratio

SAHI 是一个切片推理框架和计算机视觉流水线，旨在检测高分辨率图像中的小物体。它提供了一个将大图像划分为重叠补丁的系统，以防止标准模型缩小时通常发生的细节丢失，并附带图像平铺工具和 COCO 数据集工具包。 该项目的独特之处在于提供了一个模型无关的预测封装器，将不同的机器学习框架标准化为统一接口。这使得它能够在保持一致输出格式的同时，跨各种模型后端实现切片推理和目标检测。 除了推理之外，该框架还涵盖了 COCO 和 YOLO 格式的数据集管理，包括用于标注图像切片、类别重映射和数据集合并的工具。它还包括一个用于模型性能评估和监控的套件，具有精度和召回率指标计算、检测错误分析以及结果可视化功能。 该工具集可通过命令行接口访问，用于自动化跨图像目录和视频流的推理工作流。

mmaction2 是一个 PyTorch 视频理解工具箱，专为训练和评估深度学习模型而设计。它作为一个用于动作识别、时间定位和时空动作检测的框架，为基于像素的视频分析和基于骨架的动作识别提供了专业工具。 该项目以其模块化架构而著称，具有基于注册表的组件发现和分层、配置驱动的模型组装功能。它支持多模态特征融合，集成 RGB 帧、光流和音频，并包括用于文本到视频片段检索和零样本视频预测的功能。 该框架广泛涵盖了视频数据集工程，包括标注标准化和帧采样，以及全面的模型训练和评估。它提供用于分布式训练、知识蒸馏和通过模型重参数化进行推理优化的实用程序。 该代码库支持 ONNX 模型导出和用于跨不同计算节点部署的环境容器化。

mmtracking is a PyTorch video perception framework designed for training and deploying computer vision models that analyze sequential image data. It provides specialized tools for multi-object tracking, video instance segmentation, and a configuration-driven system for managing deep learning models. The project utilizes a deep learning model registry and a configuration-driven pipeline to swap model backbones and detectors without modifying the core codebase. This modular approach allows for the development of custom perception architectures by combining various components and configurations.

该项目是一个计算机视觉系统，专为使用实时摄像头馈送进行人脸识别和身份追踪而设计。它提供了一个框架，通过将实时视频输入与预注册人脸描述符的本地数据库进行比较，同时捕获、注册和识别多个人员。 该系统通过一个性能导向的处理管道脱颖而出，该管道在实时分析过程中平衡计算负载。通过将深度神经网络特征提取与基于质心的对象追踪相结合，该软件在视频帧间保持一致的身份标签，同时最大限度地减少昂贵识别计算的频率。这种方法允许在不需要对每一帧进行完整处理的情况下，实现对多人的稳定追踪和识别。 该库支持一系列身份管理任务，包括创建可搜索的人脸数据库和人员的自动记录。它处理生物识别数据的整个生命周期，从最初从摄像头图像中提取唯一的数值向量，到将这些描述符持久化存储在本地文件系统中以供将来验证。

This application is a real-time computer vision system designed to identify and label objects within live video feeds, recorded files, and static images. It functions as a comprehensive framework that integrates pre-trained machine learning models with video processing pipelines to perform multi-object localization and visual data tracking. The system distinguishes itself through a multithreaded architecture that decouples frame acquisition from detection logic, ensuring the interface remains responsive during continuous analysis. It provides specialized scripts for training and optimizing cu