基于optitrack location_real_PX4_yolov5的无人机目标跟踪.zip

<div align="center"> <p> <a align="center" href="https://ultralytics.com/yolov5" target="_blank"> <img width="850" src="https://github.com/ultralytics/assets/raw/master/yolov5/v62/splash_readme.png"></a> <br><br> <a href="https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app" style="text-decoration:none;"> <img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/google-play.svg" width="15%" alt="" /></a>&nbsp; <a href="https://apps.apple.com/xk/app/ultralytics/id1583935240" style="text-decoration:none;"> <img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/app-store.svg" width="15%" alt="" /></a> </p> English | [绠�浣撲腑鏂嘳(.github/README_cn.md) <br> <div> <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="CI CPU testing"></a> <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a> <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a> <br> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> <a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a> </div> <br> <p> YOLOv5 馃殌 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a> open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development. </p> <div align="center"> <a href="https://github.com/ultralytics" style="text-decoration:none;"> <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-github.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" /> <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;"> <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-linkedin.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" /> <a href="https://twitter.com/ultralytics" style="text-decoration:none;"> <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-twitter.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" /> <a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;"> <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-producthunt.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" /> <a href="https://youtube.com/ultralytics" style="text-decoration:none;"> <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-youtube.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" /> <a href="https://www.facebook.com/ultralytics" style="text-decoration:none;"> <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-facebook.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" /> <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;"> <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-instagram.png" width="2%" alt="" /></a> </div> </div> ## <div align="center">Documentation</div> See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment. ## <div align="center">Quick Start Examples</div> <details open> <summary>Install</summary> Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.7.0**](https://www.python.org/) environment, including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). ```bash git clone https://github.com/ultralytics/yolov5 # clone cd yolov5 pip install -r requirements.txt # install ``` </details> <details open> <summary>Inference</summary> YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). ```python import torch # Model model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5n - yolov5x6, custom # Images img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list # Inference results = model(img) # Results results.print() # or .show(), .save(), .crop(), .pandas(), etc. ``` </details> <details> <summary>Inference with detect.py</summary> `detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. ```bash python detect.py --source 0 # webcam img.jpg # image vid.mp4 # video path/ # directory 'path/*.jpg' # glob 'https://youtu.be/Zgi9g1ksQHc' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream ``` </details> <details> <summary>Training</summary> The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) results. [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the largest `--batch-size` possible, or pass `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB. ```bash python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128 yolov5s 64 yolov5m 40 yolov5l 24 yolov5x 16 ``` <img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png"> </details> <details open> <summary>Tutorials</summary> - [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)聽 馃殌 RECOMMENDED - [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)聽 鈽橈笍 RECOMMENDED - [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475) - [PyTorch Hub](https://gi