Awesome GitHub RepositoriesDetection Model Validation

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

This project provides a transformer-based object detection model that treats the task as a direct set prediction problem. It implements a vision system capable of predicting bounding boxes and class labels for objects within an image, as well as frameworks for instance and panoptic segmentation. The architecture utilizes a transformer encoder and decoder to perform end-to-end set prediction, employing a Hungarian matcher to assign predicted boxes to ground truth objects. It incorporates a convolutional backbone for feature extraction and a system of learnable object queries to probe image loc

PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti

YOLOv10 is a PyTorch computer vision library and real-time vision framework designed for locating and identifying multiple objects in images and video streams. It functions as an end-to-end object detector that optimizes for high-speed deployment and detection precision. The project is distinguished by an NMS-free detection architecture that predicts a single bounding box per object, eliminating the need for non-maximum suppression post-processing to reduce inference latency. It further optimizes for edge hardware through scalable weights and a quantization-friendly structure that facilitates

This project is an automated machine learning framework and toolkit designed for training and tuning custom models for classification, regression, and recommendations. It functions as a multimodal machine learning toolkit capable of processing and training models using a combination of text, image, audio, and sensor data. The framework distinguishes itself as a multimodal data processor that can handle and visualize large datasets on a single machine using column-oriented disk storage. It includes a core machine learning model generator that converts trained models into formats compatible wit

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

PyOD is a Python anomaly detection library used to identify outliers in tabular, time series, graph, text, and image data. It provides a collection of algorithms for detecting anomalous data points and includes a unified detector interface that standardizes input and output signatures across its available detection algorithms. The project features a multi-modal outlier detector for identifying anomalies across diverse formats including unstructured text and images, as well as a specialized toolkit for graph-based and time-series anomaly detection. It includes an ensemble framework for combini

هذا المشروع عبارة عن إطار عمل لاكتشاف الكائنات في PyTorch ينفذ بنية Faster R-CNN. يعمل كنموذج رؤية للتنبؤ بصناديق التحديد الدقيقة حول كائنات متعددة داخل الصور وتدفقات الفيديو الحية. تم تحسين النظام للتدريب متعدد الـ GPU لتقليل الوقت المطلوب لتقارب النموذج. يستخدم تصميماً مسرعاً بواسطة GPU للتعامل مع تدريب واستنتاج شبكات الاكتشاف المعقدة. يغطي إطار العمل دورة حياة اكتشاف الكائنات الكاملة، بما في ذلك تدريب الشبكة المخصص والاستنتاج للصور الثابتة وتدفقات الفيديو في الوقت الفعلي. يتضمن قدرات للتحقق من أداء النموذج باستخدام مجموعات بيانات قياسية، بالإضافة إلى تحسينات التدريب مثل التجميع القائم على نسبة العرض إلى الارتفاع وأحمال العمل الموزعة.

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

CenterNet هو إطار عمل لاكتشاف الكائنات بنقطة المركز وخط أنابيب رؤية حاسوبية في الوقت الفعلي. يحدد الكائنات والوضعيات من خلال التنبؤ بنقاط المركز بدلاً من استخدام صناديق التثبيت (Anchor boxes). يعمل النظام كمقدر لصناديق الإحاطة ثلاثية الأبعاد، ونموذج لتقدير وضعية الإنسان، وأداة لاكتشاف الكائنات في الوقت الفعلي. يعامل وضع المفاصل ومواقع الكائنات كمشكلات اكتشاف نقطة المركز لتحديد الكيانات في الصور والفضاء ثلاثي الأبعاد. تغطي القدرات اكتشاف الكائنات ثلاثية الأبعاد، وتقدير النقاط الرئيسية للإنسان، وتحليل الفيديو المباشر. يستخدم خط الأنابيب عملية استدلال تغذية أمامية أحادية المرحلة لإجراء تحليل مستمر على كاميرات الويب أو ملفات الفيديو.

This project provides a compact neural network architecture designed for human face detection and facial landmark localization. It functions as a specialized computer vision tool that identifies faces and extracts five specific facial key points within a single inference pass, making it suitable for integration into resource-constrained environments. The system utilizes a lightweight convolutional backbone and an anchor-based detection mechanism to maintain a small memory footprint while performing real-time processing. By employing a multi-task learning head, the model simultaneously predict