YOLOv5多品种鱼分类

<div align="center"> <p> <a align="center" href="https://ultralytics.com/yolov5" target="_blank"> <img width="850" src="https://raw.githubusercontent.com/ultralytics/assets/master/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/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">Ultralytics Live Session</div> <div align="center"> [Ultralytics Live Session Ep. 2](https://youtu.be/QGRtEG7UjtE) ✨ will be streaming live on **Tuesday, December 13th at 19:00 CET** with [Joseph Nelson](https://github.com/josephofiowa) of [Roboflow](https://roboflow.com/?ref=ultralytics) who will join us to discuss the brand new Roboflow x Ultralytics HUB integration. Tune in to ask Glenn and Joseph about how you can make speed up workflows with seamless dataset integration! 🔥 <a align="center" href="https://youtu.be/QGRtEG7UjtE" target="_blank"> <img width="800" src="https://user-images.githubusercontent.com/85292283/205996456-bf3efa33-9c46-455e-b322-a64886cc7a0b.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 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 | | [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-s