13 مستودعات
Automated workflows designed to process, normalize, and manipulate facial or visual data for machine learning tasks.
Explore 13 awesome GitHub repositories matching artificial intelligence & ml · Computer Vision Pipelines. Refine with filters or upvote what's useful.
Faceswap is a comprehensive framework for automated media manipulation and neural face synthesis. It provides a modular pipeline that manages the entire lifecycle of facial feature extraction, deep learning model training, and image conversion. By coordinating complex computer vision workflows, the system enables users to map facial identities between source and destination datasets while maintaining structural alignment and lighting consistency across video frames. The project distinguishes itself through a highly extensible plugin-based architecture that handles hardware-accelerated process
Reconstructs facial imagery from source frames by reapplying alignment metadata and refined transformation parameters.
Facefusion is a modular framework designed for automated image and video manipulation, specializing in tasks such as face swapping, enhancement, and restoration. It functions as a computer vision processing pipeline that chains independent machine learning modules to perform complex transformations, including facial animation, age modification, and lip synchronization. The system is built to handle both real-time interactive feeds and large-scale batch processing tasks. The platform distinguishes itself through a highly extensible architecture that supports custom processing modules and inter
Orchestrates complex image and video analysis through a modular computer vision processing pipeline.
This project is a deep learning curriculum and a collection of PyTorch tutorials designed for deep learning education. It provides a structured set of technical documents and runnable notebooks that translate theoretical machine learning concepts into executable code. The repository includes implementation guides for various neural network architectures, specifically covering convolutional, recurrent, and transformer-based models. It provides practical examples for building computer vision pipelines for object detection and semantic segmentation, as well as natural language processing tools f
Provides automated workflows for image processing, including object detection and semantic segmentation.
Openface is a deep learning toolkit designed for facial recognition and identity verification. It provides a comprehensive pipeline for detecting faces, aligning landmarks, and transforming facial images into compact numerical vectors. By utilizing these embeddings, the system enables identity classification and similarity comparison through geometric distance calculations. The project distinguishes itself by integrating research-oriented diagnostic tools alongside its core recognition capabilities. It includes utilities for visualizing high-dimensional feature clusters, inspecting internal c
Normalizes facial orientation and locates landmarks to prepare image data for deep learning recognition models.
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
Provides automated workflows for object detection, semantic segmentation, and image classification.
This project is a collection of TensorFlow 2.x machine learning tutorials and practical code examples. It serves as a deep learning implementation guide for constructing diverse neural network architectures, including convolutional, recurrent, and generative networks. The repository provides templates and examples for several specialized domains, including computer vision for image classification and object detection, natural language processing for text generation and language understanding, and generative AI for synthesizing data using adversarial networks and autoencoders. It also includes
Provides automated workflows to process and manipulate visual data for machine learning tasks.
Tensorpack هو إطار عمل شبكة عصبية TensorFlow عالي المستوى ومكتبة بحثية مصممة لبناء وتدريب نماذج التعلم العميق. يوفر مجموعة من بنيات الشبكات العصبية القابلة للتكرار للرؤية الحاسوبية، والمهام التوليدية، والتعلم التعزيزي، ومعالجة اللغات الطبيعية. يتميز المشروع بخط معالجة بيانات تعلم عميق متخصص يستخدم Python الخالص لتحميل البيانات المتوازي والبث. ويتضمن منسق تدريب متعدد وحدات GPU لتوزيع أعباء العمل عبر استراتيجيات موازية للبيانات ومجموعة أدوات قابلية تفسير مخصصة لتصور خرائط بروز وتنشيط النموذج. يغطي إطار العمل مجموعة واسعة من القدرات، بما في ذلك خطوط معالجة الرؤية الحاسوبية لاكتشاف الكائنات والتجزئة الدلالية، ونمذجة التسلسل للكلام والنص، وتطوير وكيل التعلم التعزيزي. كما يوفر أدوات تحسين النموذج لتكميم الأوزان والتدريب منخفض البت، إلى جانب مرافق لإعادة إنتاج الأوراق البحثية الأكاديمية وتحويل أوزان نموذج Caffe القديمة.
Ships automated workflows for processing and manipulating visual data for classification, detection, and generative tasks.
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
Provides a toolkit of pre-built pipelines for image classification, detection, segmentation, and pose estimation.
SAHI هو إطار عمل للاستدلال المقطع (sliced inference) وخط معالجة رؤية الكمبيوتر مصمم لاكتشاف الأشياء الصغيرة في الصور عالية الدقة. يوفر نظاماً لتقسيم الصور الكبيرة إلى رقع متداخلة لمنع فقدان التفاصيل الذي يحدث عادةً أثناء تقليل حجم النموذج القياسي، إلى جانب أداة تبليط الصور ومجموعة أدوات بيانات COCO. يتميز المشروع بتقديم غلاف تنبؤ محايد للنموذج يوحد أطر عمل تعلم الآلة المختلفة في واجهة موحدة. يسمح هذا بتنفيذ الاستدلال المقطع واكتشاف الأشياء عبر خلفيات نماذج مختلفة مع الحفاظ على تنسيق مخرجات متسق. بعيداً عن الاستدلال، يغطي إطار العمل إدارة مجموعات البيانات لتنسيقات COCO و YOLO، بما في ذلك أدوات لتقطيع الصور المشروحة، وإعادة تعيين الفئات، ودمج مجموعات البيانات. كما يتضمن مجموعة لتقييم ومراقبة أداء النموذج، تتميز بحساب مقاييس الدقة والاستدعاء، وتحليل خطأ الاكتشاف، وتصور النتائج. مجموعة الأدوات متاحة عبر واجهة سطر الأوامر لأتمتة سير عمل الاستدلال عبر أدلة الصور وتدفقات الفيديو.
Implements a standardized workflow for managing object detection and segmentation across various ML frameworks and backends.
MuseTalk is a deep learning lip synchronization system designed to align video facial movements with audio tracks for high-fidelity video dubbing. It functions as an engine that matches facial expressions to audio input in real-time, enabling the modification of a speaker's lip movements to match new audio sources across different languages. The project features a distributed GPU training pipeline and a multi-stage processing workflow for refining the visual accuracy of synthetic speech. It distinguishes itself through the use of region-specific face masking and mouth openness control, which
Provides utilities to isolate the mouth and jaw areas via region-specific masking to preserve subject identity.
This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi
Implements image classification pipelines using image augmentation and convolutional neural networks.
This repository is a comprehensive educational program and deep learning framework designed to teach practical deep learning using PyTorch through notebooks and code examples. It serves as a high-level library for building, training, and deploying neural networks, acting as a model training orchestrator that coordinates PyTorch models, optimizers, and loss functions. The project provides specialized toolkits for computer vision, natural language processing, and tabular data preprocessing. It distinguishes itself through advanced training controls such as discriminative learning rates, a two-w
Processing image data and building models for tasks like image classification and semantic segmentation.
LightGlue هو إطار عمل للتعلم العميق مصمم لمطابقة الميزات المحلية وتقدير المراسلات عالية السرعة بين أزواج الصور. يعمل كنموذج مطابقة للرؤية الحاسوبية يحدد النقاط الرئيسية المتقابلة عبر وجهات نظر مختلفة. يستخدم النظام بنية شبكة عصبية تكيفية تعمل على تحسين سرعة الاستدلال ديناميكيًا عن طريق تقليم عمقها وعرضها بناءً على أزواج الصور المدخلة. يستخدم هذا النهج آلية انتباه بنمط transformer وانتباه عبر الصور لحساب الارتباطات بين واصفات الميزات. تتضمن عملية المطابقة حلقة تحسين تكرارية وإيقافًا مبكرًا ديناميكيًا لإيقاف الحساب بمجرد استيفاء عتبات الثقة. تدعم هذه القدرات خط أنابيب رؤية حاسوبية أوسع لمحاذاة الصور في الوقت الفعلي وتحسين استدلال الشبكة العصبية.
Processes image data to identify shared landmarks for use in broader computer vision workflows.