v8.2.0

版本发布时间: 2024-04-17 13:32:48

ultralytics/assets最新发布版本:v8.2.0(2024-04-17 13:32:48)

Ultralytics v8.2.0 Release Notes

Introduction

Ultralytics is excited to announce the v8.2.0 release of YOLOv8, comprising 277 merged Pull Requests by 32 contributors since our last v8.1.0 release in January 2024, marking another milestone in our journey to make state-of-the-art AI accessible and powerful. This release brings a host of new features, performance optimizations, and expanded integrations, reflecting our commitment to continuous improvement and innovation. 🌍🚀

Ultralytics v8.2.0 Key Highlights

New Models: Introduced support for YOLOv8-World, YOLOv8-World-v2 (by @Laughing-q in PR #9268), YOLOv9-C, YOLOv9-E (by @Laughing-q in PR #8571), and YOLOv9 Segment models (by @Burhan-Q in PR #9296), expanding the versatility of the Ultralytics platform.
New Features: Added distance calculation in vision-eye, per-class object counting (by @RizwanMunawar in PR #9443), and queue management utilities (by @RizwanMunawar in PR #9494), enhancing the functionality and applicability of YOLOv8.
Performance Optimizations: Achieved 40% faster ultralytics imports (by @glenn-jocher in PR #9547), faster batch same_shapes, and immediate checkpoint serialization (by @glenn-jocher in PR #9437), further optimizing the efficiency of the framework.
Enhanced Export Capabilities: Improved export support, including OpenVINO 2023.3 updates (by @adrianboguszewski in PR #8417), TensorRT 10 support (by @Burhan-Q in PR #9516), and fixes for TFLite, ONNX, and OpenVINO exports.
Documentation Expansion: Significantly expanded the documentation with new guides, integration pages for TorchScript, TFLite, NCNN, PaddlePaddle, TF GraphDef, TF SavedModel, TF.js (by @abirami-vina in multiple PRs), and updates to existing pages, providing comprehensive resources for users.
Training Enhancements: Introduced YOLO-World training support (by @Laughing-q in PR #9268), fixed learning rate issues (by @Laughing-q in PR #9468), and improved robustness for stopping and resuming training (by @glenn-jocher in PR #9384).
Platform Support: Added support for NVIDIA Jetson (by @lakshanthad in PR #9484), Raspberry Pi (by @lakshanthad in PR #8828), and Apple M1 runners for tests and benchmarks (by @glenn-jocher in PR #8162), expanding the usability of YOLOv8 across various platforms.
CI/CD Improvements: Enhanced Ultralytics Actions using OpenAI GPT-4 for PR summaries (by @pderrenger in PR #7867) and introduced self-hosted Raspberry Pi 5 CI (by @lakshanthad in PR #8828), streamlining the development and testing processes.
Bug Fixes: Resolved various issues related to model loading, inference, plotting, and exports, ensuring a smoother user experience.
Community Contributions: Welcomed contributions from 31 new contributors, reflecting the growing engagement and collaborative spirit within the Ultralytics community.

Summary

Ultralytics v8.2.0 represents a significant leap forward, introducing new models, features, and optimizations while expanding platform support and integration capabilities. We extend our gratitude to our dedicated users and contributors for their invaluable support and contributions. As we continue to push the boundaries of AI and computer vision, we look forward to the exciting possibilities and advancements that lie ahead! 🌟🚀🎉