rtdetr

[CVPR 2024] Official RT-DETR (RTDETR paddle pytorch), Real-Time DEtection TRansformer, DETRs Beat YOLOs on Real-time Object Detection. 🔥 🔥 🔥

NVIDIA DeepStream SDK 8.0 / 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

Python library for YOLO small object detection and instance segmentation

YOLO SHOW - YOLOv11 / YOLOv10 / YOLOv9 / YOLOv8 / YOLOv7 / YOLOv5 / RTDETR / SAM / MobileSAM / FastSAM YOLO GUI based on Pyside6

yolov3, yolo12, dino, segmenations, face, pose, keypoints on deepstream

A repo that uses TensorRT to deploy wll-trained models.Support RTDETR,YOLO-NAS,YOLOV5,YOLOV6,YOLOV7,YOLOV8,YOLOX.

C++ implementations of YOLOv8/YOLO26, RT-DETR-V2(RTDETR), MobileSAM, and NanoSAM on TensorRT, ONNX Runtime, and RKNN.

RT-DETR for pytorch

Enhancing Tiny Object Detection Using Guided Object Inference Slicing (GOIS): An Efficient Dynamic Adaptive Framework for Fine-Tuned and Non-Fine-Tuned Deep Learning Models

Unified PyTorch implementations of RT-DETRv3, D-FINE, DEIM, RT-DETRv4 & DEIMv2 — 30 COCO variants, one CLI for train/eval/infer/export. DETR 系列实时目标检测模型统一 PyTorch 实现

A method to optimize the current SAR ship detection pipeline using the DEtection TRansformer (DETR) model.

TensorRT-DETR is a C++/CUDA/TensorRT inference deployment library for NVIDIA GPUs. It provides C++ and Python APIs for detection, instance segmentation, pose estimation.

Source-wise PCB defect detection benchmark with YOLOv11s, RT-DETR, Faster R-CNN, and a cleaned 6-class merged dataset.

YOLOv8 vs RT-DETR: PyTorch/ONNX/TensorRT benchmarks on RTX 5080

Final Year Project repository, where we developed a robust detection and tracking system using LiDAR data to run on the Jetson Xavier.

3rd-place private leaderboard solution for Kaggle FathomNet 2026, including source code, model checkpoints, final submissions, reproducibility notes, and writeup

This repository enables training Ultralytics object detection models with the anchor-free LADA assignment algorithm and DFL-based regression. Multi-model feature fusion, joint training, and hybrid inference are also supported.

Modular computer vision framework powering detection, tracking, segmentation, and surveillance AI systems.

Detector de somnolencia - Ultralytics RTDETR

Local-first Windows vision workbench: camera capture, annotation, dataset review and splits, versioned training, evaluation, and tqdm-based ETA.