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

This project is a PyTorch object detection framework that implements the Faster R-CNN architecture. It serves as a vision model for predicting precise bounding boxes around multiple objects within images and live video feeds. The system is optimized for multi-GPU training to reduce the time required for model convergence. It utilizes a GPU-accelerated design to handle the training and inference of complex detection networks. The framework covers the full object detection lifecycle, including custom network training and inference for static images and real-time video streams. It includes capa

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 is a sliced inference framework and computer vision pipeline designed to detect small objects in high-resolution images. It provides a system for dividing large images into overlapping patches to prevent the detail loss that typically occurs during standard model downscaling, alongside an image tiling utility and a COCO dataset toolkit. The project distinguishes itself by offering a model-agnostic prediction wrapper that standardizes different machine learning frameworks into a unified interface. This allows it to implement sliced inference and object detection across various model backe

mmaction2 एक PyTorch वीडियो अंडरस्टैंडिंग टूलबॉक्स है जिसे डीप लर्निंग मॉडल्स को प्रशिक्षित और मूल्यांकन करने के लिए डिज़ाइन किया गया है। यह एक्शन रिकग्निशन, टेम्पोरल लोकलाइजेशन और स्पैटियो-टेम्पोरल एक्शन डिटेक्शन के लिए एक फ्रेमवर्क के रूप में कार्य करता है, जो पिक्सेल-आधारित वीडियो विश्लेषण और कंकाल-आधारित एक्शन रिकग्निशन दोनों के लिए विशेष टूल्स प्रदान करता है। यह प्रोजेक्ट रजिस्ट्री-आधारित घटक खोज और पदानुक्रमित, कॉन्फ़िगरेशन-संचालित मॉडल असेंबली की विशेषता वाले मॉड्यूलर आर्किटेक्चर के माध्यम से खुद को अलग करता है। यह मल्टी-मोडल फीचर फ्यूजन का समर्थन करता है, RGB फ्रेम्स, ऑप्टिकल फ्लो और ऑडियो को एकीकृत करता है, और टेक्स्ट-टू-वीडियो क्लिप रिट्रीवल और ज़ीरो-शॉट वीडियो भविष्यवाणी के लिए क्षमताएं शामिल करता है। व्यापक रूप से, यह फ्रेमवर्क वीडियो डेटासेट इंजीनियरिंग को कवर करता है, जिसमें एनोटेशन मानकीकरण और फ्रेम सैंपलिंग, साथ ही व्यापक मॉडल प्रशिक्षण और मूल्यांकन शामिल है। यह डिस्ट्रीब्यूटेड प्रशिक्षण, नॉलेज डिस्टिलेशन और मॉडल रिपैरामीट्राइजेशन के माध्यम से इन्फरेंस ऑप्टिमाइज़ेशन के लिए यूटिलिटीज प्रदान करता है। कोडबेस विभिन्न कंप्यूट नोड्स में डिप्लॉयमेंट के लिए ONNX मॉडल एक्सपोर्ट और एनवायरनमेंट कंटेनरीकरण का समर्थन करता है।

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.

This project is a computer vision system designed for real-time facial recognition and identity tracking using live camera feeds. It provides a framework for capturing, registering, and identifying multiple individuals simultaneously by comparing live video input against a local database of pre-registered facial descriptors. The system distinguishes itself through a performance-oriented processing pipeline that balances computational load during live analysis. By combining deep neural network feature extraction with centroid-based object tracking, the software maintains consistent identity la

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