Glenn Jocher

Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking

Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.

YOLOv10: Real-Time End-to-End Object Detection [NeurIPS 2024]

PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.

Legacy JSON-to-YOLO dataset converter for COCO, LabelMe, Labelbox, VoTT, INFOLKS, and ATH annotations. Superseded by convert_coco() in the Ultralytics package.

:art: Pytorch YOLO v5 训练自己的数据集超详细教程！！！ :art: (提供PDF训练教程下载）

KAPAO is an efficient single-stage human pose estimation model that detects keypoints and poses as objects and fuses the detections to predict human poses.

based on yolo-high-level project (detect\pose\classify\segment\):include yolov5\yolov7\yolov8\ core ,improvement research ,SwintransformV2 and Attention Series. training skills, business customization, engineering deployment C

[CVPR2020] GhostNet: More Features from Cheap Operations

Ultralytics YOLO iOS app and Swift package for real-time Core ML inference across major computer vision tasks.

Official Ultralytics YOLO Flutter plugin for real-time inference on Android and iOS across major vision tasks.