YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Ultralytics YOLO11 🚀
YOLOv3 in PyTorch > ONNX > CoreML > TFLite
NVIDIA DeepStream SDK 7.1 / 7.0 / 6.4 / 6.3 / 6.2 / 6.1.1 / 6.1 / 6.0.1 / 6.0 / 5.1 implementation for YOLO models
🚀🚀🚀 A collection of some awesome public YOLO object detection series projects.
Open Source Generative Process Automation (i.e. Generative RPA). AI-First Process Automation with Large ([Language (LLMs) / Action (LAMs) / Multimodal (LMMs)] / Visual Language (VLMs)) Models
🚀 你的YOLO部署神器。TensorRT Plugin、CUDA Kernel、CUDA Graphs三管齐下，享受闪电般的推理速度。| Your YOLO Deployment Powerhouse. With the synergy of TensorRT Plugins, CUDA Kernels, and CUDA Graphs, experience lightning-fast inference speeds.
Easy & Modular Computer Vision Detectors, Trackers & SAM - Run YOLOv9,v8,v7,v6,v5,R,X in under 10 lines of code.
Ultralytics YOLOv8, YOLOv9, YOLOv10, YOLOv11 for ROS 2
🚀 Use YOLO11 in real-time for object detection tasks, with edge performance ⚡️ powered by ONNX-Runtime.
Packaged version of ultralytics/yolov5 + many extra features
xView 2018 Object Detection Challenge: YOLOv3 Training and Inference.
Converting COCO annotation (CVAT) to annotation for YOLOv8-seg (instance segmentation) and YOLOv8-obb (oriented bounding box detection)
ROS/ROS 2 package for Ultralytics YOLOv8 real-time object detection and segmentation. https://github.com/ultralytics/ultralytics
A PyTorch implementation of Spiking-YOLOv3. Two branches are provided, based on two common PyTorch implementation of YOLOv3(ultralytics/yolov3 & eriklindernoren/PyTorch-YOLOv3), with support for Spiking-YOLOv3-Tiny at present.
Easy-to-use finetuned YOLOv8 models.
Ultralytics YOLO iOS App source code for running YOLOv8 in your own iOS apps 🌟
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