毕业设计深度学习基于步态识别检测的多目标跨镜头跟踪算法研究项目源码.zip

<div align="center"> <p> <a align="left" href="https://ultralytics.com/yolov5" target="_blank"> <img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a> </p> <br> <div> <a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/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 🚀 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"> <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="2%"/> </a> <img width="2%" /> <a href="https://www.linkedin.com/company/ultralytics"> <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="2%"/> </a> <img width="2%" /> <a href="https://twitter.com/ultralytics"> <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="2%"/> </a> <img width="2%" /> <a href="https://www.producthunt.com/@glenn_jocher"> <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png" width="2%"/> </a> <img width="2%" /> <a href="https://youtube.com/ultralytics"> <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/> </a> <img width="2%" /> <a href="https://www.facebook.com/ultralytics"> <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/> </a> <img width="2%" /> <a href="https://www.instagram.com/ultralytics/"> <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="2%"/> </a> </div> <!-- <a align="center" href="https://ultralytics.com/yolov5" target="_blank"> <img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a> --> </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> Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) . [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 yolov5m, yolov5l, yolov5x, 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)&nbsp; 🚀 RECOMMENDED * [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)&nbsp; ☘️ RECOMMENDED * [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)&nbsp; 🌟 NEW * [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975)&nbsp; 🌟 NEW * [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475) * [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)&nbsp; ⭐ NEW * [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀 * [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303) * [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318) * [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304) * [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607) * [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)&nbsp; ⭐ NEW * [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx) </details> ## <div align="center">Environments</div> Get started in seconds with our verified environments. Click each icon below for details. <div align="center"> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"> <img src="https://github.com/ultralytics/yolov5/r