Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
- ultralytics
- yolov8
- yolo
- deep-learning
Repository Index
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Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
- yolov5
- object-detection
- pytorch
- onnx
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
OpenMMLab Detection Toolbox and Benchmark
- object-detection
- instance-segmentation
- fast-rcnn
- faster-rcnn
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
- mask-rcnn
- tensorflow
- object-detection
- instance-segmentation
Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
- open-vocabulary-detection
- open-vocabulary-segmentation
- data-generation
- automatic-labeling-system
Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
- object-detection
- instance-segmentation
- faster-rcnn
- mask-rcnn
Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
- scaled-yolov4
- yolor
- yolov3
- yolov4
The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
- object-detection
- open-world
- open-world-detection
- vision-language
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
- yolox
- yolov3
- onnx
- tensorrt
PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.
- yolov3
- object-detection
- yolo
- deep-learning
RF-DETR is a real-time object detection and segmentation model architecture developed by Roboflow, SOTA on COCO, designed for fine-tuning. [ICLR 2026]
Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version
A faster pytorch implementation of faster r-cnn
- faster-rcnn
- pytorch
Minimal PyTorch implementation of YOLOv3
OpenMMLab's next-generation platform for general 3D object detection.
- pytorch
- 3d-object-detection
- object-detection
- point-cloud
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
- deep-neural-networks
- deep-learning
- object-detection
- anchor-free
YOLOv6: a single-stage object detection framework dedicated to industrial applications.
OpenPCDet Toolbox for LiDAR-based 3D Object Detection.
- point-cloud
- 3d-detection
- autonomous-driving
- pv-rcnn
[CVPR 2024] Official RT-DETR (RTDETR paddle pytorch), Real-Time DEtection TRansformer, DETRs Beat YOLOs on Real-time Object Detection. 🔥 🔥 🔥
- rtdetr
- rtdetrv2
Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
- object-detection
- instance-segmentation
- computer-vision
- small-object-detection
The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.
- efficientdet
- efficientnet
- pytorch
- object-detection
A PyTorch Implementation of Single Shot MultiBox Detector