#YOLO

Explore a comprehensive Python package designed for efficient computer vision applications, offering tools for easy dataset management, drawing detections, and zone counting. This model-agnostic package is compatible with YOLO and other popular models, featuring connectors for widely-used libraries. Customizable annotators and utilities support various formats, such as YOLO, Pascal VOC, and COCO, making it ideal for developers seeking streamlined solutions for classification, detection, and segmentation. Benefit from thoroughly detailed tutorials and practical examples, and become part of the open-source community driving advancements in computer vision.

Darkflow utilizes the YOLO framework to deliver efficient real-time object detection and classification. Compatible with Python 3, TensorFlow, and OpenCV, it provides GPU and CPU support. Features include easy installation, flexible model configurations, and JSON output, making it ideal for scalable object detection across applications.

Access a wide array of tutorials on leading computer vision models and methodologies. The repository includes education on models from ResNet and YOLO to more sophisticated types like DETR, Grounded DINO, SAM, and GPT-4 Vision. Gain knowledge on model fine-tuning, instance segmentation, and using object-detection transformers. Through thorough tutorials and practical examples, this resource supports both novices and experts in enhancing their skills across various vision tasks, leveraging platforms like Colab and Kaggle. Empower your computer vision initiatives with comprehensive tutorials and experiments available here.

MMYOLO is an open-source toolbox in the OpenMMLab ecosystem designed for implementing YOLO series algorithms. It supports features like YOLOv5 instance segmentation and the real-time object detector RTMDet. The platform is optimized for speed and accuracy in tasks such as object detection, rotated object detection, and instance segmentation. Developed on PyTorch and MMDetection, it boasts extensive documentation and modular design. Training is significantly accelerated, achieving speeds 2.6 times faster than earlier versions. This toolkit is ideal for developers focusing on high-performance computer vision solutions.

Flickr Scraper is a specialized Python tool designed for collecting images from Flickr to optimize YOLO model datasets. With a user-focused approach, it allows for downloading images directly based on search criteria, making dataset assembly straightforward. Installation and operation are simple, facilitating quick access to necessary images while following Flickr's API guidelines. Suitable for data scientists and AI researchers, it streamlines the creation of training datasets for computer vision tasks.

Delve into the pivotal AI breakthroughs of 2020 through an extensive compilation of key research papers. Gain insight into advancements such as ethical AI considerations, image creation, code translation, and video repair. Each entry in this curated list is enriched with video summaries, code examples, and detailed article links, offering a vital resource for both enthusiasts and professionals eager to track the evolution of AI.

Explore an extensive compilation of essential articles and resources on object detection, highlighting influential models such as R-CNN, YOLO, and SSD. This collection spans advancements from 2013 to 2019, providing insights into deep learning developments in object detection through papers, code, and comprehensive surveys. Examine subjects like real-time object detection, zero-shot detection, and weakly supervised methodologies, offering valuable information for those interested in cutting-edge object detection technologies.

The TensorRT-YOLO project supports enhanced inference for YOLOv3 to YOLO11 and PP-YOLOE models through NVIDIA TensorRT optimization. It integrates TensorRT plugins, CUDA kernels, and CUDA Graphs to deliver a fast object detection solution compatible with C++ and Python. Key features include ONNX export, command-line model export, and Docker deployment.

A versatile video labeling interface built with PyQt5, integrating detectors and trackers such as YOLO, OpenPifPaf, and SiamMask for effective object recognition and monitoring. Supports SSH connections to GPU servers, dark mode, and adjustable bounding boxes. Utilizes the Hungarian algorithm for accurate track ID assignment, enhancing labeling precision. Features streamlined import/export of CSV label files and intuitive keyboard/mouse controls for improved efficiency. Suitable for precise video annotation projects, with comprehensive installation and configuration support.

This collection offers a variety of resources for deep learning-based object detection and tracking. It includes research papers addressing both static and dynamic detection with methods like RCNN, YOLO, SSD, and RetinaNet. The resource set also expands into multi and single object tracking techniques and provides specific datasets for tasks including UAV and microscopy tracking, as well as video segmentation and motion prediction. Comprehensive code repositories and frameworks are available to assist researchers and engineers in achieving efficient and state-of-the-art results in computer vision.

Explore a vast repository of papers focused on deep learning methods for object detection. This continually updated resource includes the latest research from major conferences like CVPR, ICCV, and NeurIPS, offering perspectives on the development of deep learning object detection. Review detailed performance tables and identify key articles for researchers and enthusiasts. Learn about significant breakthroughs such as R-CNN, YOLO, and Faster R-CNN for the latest advances in object detection.

Explore an innovative annotation tool that boosts productivity through AI-driven labeling for complex visual data tasks. It supports image and video processing with seamless import/export across COCO, VOC, and YOLO formats. Featuring one-click model inference, custom model support, and GPU acceleration, it accommodates tasks like classification, detection, and segmentation. Its comprehensive model library, including YOLO and Segment Anything, delivers quality outcomes for visual data professionals.

QuickAI facilitates testing of complex Machine Learning models with minimal Python code. It supports diverse architectures like EfficientNet, VGG, ResNet, YOLO, and GPT-NEO for tasks such as image classification, NLP, and object detection. Compatible with TensorFlow and PyTorch, and offering Docker for easy setup, it simplifies development by reducing code length and enhancing productivity.

PaddleSlim offers a comprehensive library for compressing deep learning models, utilizing techniques like low-bit quantization, knowledge distillation, pruning, and neural architecture search. These methods help to optimize model size and performance on different hardware such as Nvidia GPUs and ARM chips. Key features include automated compression support for ONNX models and analytical tools for refining strategies. PaddleSlim also provides detailed tutorials and documentation for applying these methods in natural language processing and computer vision fields.

This extensive collection is a valuable resource for those interested in computer vision and AI. It provides accompanying code and insightful articles on various topics such as NLP, brain tumor segmentation, and more. Ideal for research and development, it includes practical examples in areas like LiDAR SLAM and autonomous driving, offering a seamless blend of theory and practical application.

This repository is a comprehensive resource hub for the YOLO framework, renowned for real-time object detection. It offers official implementations and variations for platforms like PyTorch and TensorFlow. The project includes extensional frameworks, lightweight deployment options, and applications across diverse fields such as video and medical detection. Covering techniques like pruning, knowledge distillation, and quantization, it supports deployment on hardware like FPGA and TPU. Developers can benefit from the curated learning resources, paper reviews, and code evaluations to enhance skills relevant to fields like autonomous driving and robotics.

The project utilizes diagrams.net for creating concise visual diagrams of neural network architectures like YOLO v1 and VGG-16. Contributions from community members enrich the repository. These visual aids help in comprehending the complexity of networks, supporting educational and developmental purposes in data science.

This repository provides a complete suite of visual assets, pre-trained models, and curated datasets that integrate smoothly with the Ultralytics YOLO ecosystem. It offers essential tools for object detection, image classification, among others, suitable for both personal and commercial applications. Users can easily download pre-trained models to perform inference with minimal effort. Additionally, it features a wide range of visual assets and datasets to facilitate diverse machine learning projects. With comprehensive documentation and varied licensing options, the repository is designed to support both hobbyists and professionals in advancing their computer vision capabilities.