#YOLOv8

BoxMOT offers state-of-the-art, flexible multi-object trackers for segmentation, detection, and pose estimation, adaptable to various hardware from CPUs to GPUs. Compatible with YOLO models, it integrates advanced ReID systems and reduces experimentation overhead with efficient data handling.

YOLOAir is a PyTorch-based YOLO algorithm library providing a unified framework for modular detection models. It incorporates models like YOLOv5, YOLOv7, YOLOv6, Transformer, and PP-YOLO, facilitating flexible customization of network components such as Backbone, Neck, and Head. This structure aids research by enabling various combinations, making it adaptable for different datasets and business scenarios. It supports multiple tasks, including object detection and image classification, aiming to balance accuracy and efficiency.

This C++ implementation of YOLOv8 via TensorRT excels in object detection, semantic segmentation, and body pose estimation. Optimized for GPU inference, the project utilizes the TensorRT C++ API and facilitates integration with ONNX models converted from PyTorch. The project runs on Ubuntu, necessitating CUDA, cudnn, and CUDA-supported OpenCV. Users will find comprehensive setup instructions, model conversion guidance, and INT8 inference optimization tips. This project is ideal for developing high-performance vision applications on NVIDIA GPUs.

Autodistill enhances AI model development by converting unlabeled images into inferable models suitable for edge deployment without manual input. Utilizing comprehensive foundation models for automatic dataset labeling, it specializes in vision tasks such as object detection and instance segmentation. Autodistill provides a modular interface for seamless model integration and deployment, making it a practical tool for developers focused on efficiency and performance.

FastSAM is an image segmentation model offering 50 times faster performance with limited data. It supports text, box, and point prompts for a user-friendly experience. The model is lightweight for efficient memory usage, and recent updates improve edge quality and add semantic labels. Available demos on HuggingFace and Replicate showcase its use in anomaly detection and more.

The project presents a streamlined model for integrating three tasks into a single framework, emphasizing efficient real-time multi-task learning. It features an Adaptive Concatenate Module designed for segmentation and a universal segmentation head, providing notable performance in practical scenarios. Through extensive testing, the model demonstrates significant improvements over existing methods in terms of inference speed and visualization, using publicly available autonomous driving datasets and actual road data. The solution is implemented with Python and PyTorch, providing clear guidance for training, evaluation, and prediction, making it a practical choice for enhancing complex autonomous driving functions.

The project offers a detailed guide for leveraging ONNX and YOLOv8 in object detection, covering essential requirements and installation procedures, with a focus on GPU compatibility using onnxruntime-gpu for NVIDIA devices. It facilitates model conversion via Google Colab using clear Python scripts. The repository features examples for image, webcam, and video inference, demonstrating the model's versatility and efficiency. This project serves as a valuable resource for developers interested in implementing YOLOv8 in ONNX format, providing practical application insights.

YOLOv8-TensorRT boosts YOLOv8 performance by employing TensorRT for faster inference. It leverages CUDA and C++ for engine construction and facilitates ONNX model export with NMS integration. This project provides flexible deployment options using Python and Trtexec on various platforms, including Jetson. The comprehensive setup guide helps adapt to different AI deployment needs, offering an efficient PyTorch alternative.

Explore a universal AI aim alignment tool leveraging DirectML, ONNX, and YOLOV8 for superior gaming performance. It offers a seamless interface and versatile customization, tailored for gamers with various challenges. Free and source-available, Aimmy fosters an inclusive gaming environment without the need for coding expertise.

FastDeploy enables efficient AI model deployment on Cloud, Mobile, and Edge platforms. It optimizes over 160 models, including text, vision, speech, and cross-modal applications. Key features include image classification, object detection, OCR, face detection, and NLP, addressing developer requirements across multiple scenarios and platforms. With recent updates for YOLOv8 and serving deployment visualization, it facilitates broad compatibility and straightforward integration into diverse environments.