yolov5对象检测项目.zip

<div align="center"> <p> <a align="center" href="https://ultralytics.com/yolov5" target="_blank"> <img width="850" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png"></a> </p> [English](README.md) | [简体中文](README.zh-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="YOLOv5 CI"></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://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <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> </div> <br> YOLOv5 🚀 is the world's most loved vision AI, representing <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. To request an Enterprise License please complete the form at <a href="https://ultralytics.com/license">Ultralytics Licensing</a>. <div align="center"> <a href="https://github.com/ultralytics" style="text-decoration:none;"> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/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/main/social/logo-social-linkedin.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/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/main/social/logo-social-twitter.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/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/main/social/logo-social-producthunt.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/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/main/social/logo-social-youtube.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/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/main/social/logo-social-facebook.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/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/main/social/logo-social-instagram.png" width="2%" alt="" /></a> </div> </div> ## <div align="center">Ultralytics Live Session</div> <div align="center"> [Ultralytics Live Session 3](https://youtu.be/IPcpYO5ITa8) ✨ is here! Join us on January 18th at 18 CET as we dive into the latest advancements in YOLOv8, and demonstrate how to use this cutting-edge, SOTA model to improve your object detection, instance segmentation, and image classification projects. See firsthand how YOLOv8's speed, accuracy, and ease of use make it a top choice for professionals and researchers alike. In addition to learning about the exciting new features and improvements of Ultralytics YOLOv8, you will also have the opportunity to ask questions and interact with our team during the live Q&A session. We encourage all of you to come prepared with any questions you may have. Don't miss out on this opportunity! To join the webinar, visit our YouTube Channel and turn on your notifications! https://youtu.be/IPcpYO5ITa8 <a align="center" href="https://youtu.be/IPcpYO5ITa8" target="_blank"> <img width="800" src="https://user-images.githubusercontent.com/26833433/212472119-7de539c1-5022-41cf-ae28-37b69158fbbe.png"></a> </div> ## <div align="center">Segmentation ⭐ NEW</div> <div align="center"> <a align="center" href="https://ultralytics.com/yolov5" target="_blank"> <img width="800" src="https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png"></a> </div> Our new YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) instance segmentation models are the fastest and most accurate in the world, beating all current [SOTA benchmarks](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco). We've made them super simple to train, validate and deploy. See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v7.0) and visit our [YOLOv5 Segmentation Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) for quickstart tutorials. <details> <summary>Segmentation Checkpoints</summary> <br> We trained YOLOv5 segmentations models on COCO for 300 epochs at image size 640 using A100 GPUs. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) notebooks for easy reproducibility. | Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Train time<br><sup>300 epochs<br>A100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TRT A100<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) | |----------------------------------------------------------------------------------------------------|-----------------------|----------------------|-----------------------|-----------------------------------------------|--------------------------------|--------------------------------|--------------------|------------------------| | [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** | | [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 | | [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 | | [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640