<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 | [ç®ä½ä¸æ](.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="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>
<p>
YOLOv5 ð 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.
<br><br>
To request a commercial license please complete the form at <a href="https://ultralytics.com/license">Ultralytics Licensing</a>.
<br><br>
</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">Segmentation â NEW</div>
<div align="center">
<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
<img width="800" src="https://user-images.githubusercontent.com/26833433/203348073-9b85607b-03e2-48e1-a6ba-fe1c1c31749c.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-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 |
- All checkpoints are trained to 300 epochs with SGD optimizer with `lr0=0.01` and `weight_decay=5e-5` at image size 640 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5_v70_official
- **Accuracy** values are for single-model single-scale on COCO dataset.<br>Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
- **Speed** averaged over 100 inference images using a [Colab Pro](https://colab.research.google.com/signup) A100 High-RAM instance. Values ind
没有合适的资源?快使用搜索试试~ 我知道了~
基于yolov5的扑克牌识别项目源码+数据集+模型
共434个文件
jpg:150个
txt:116个
py:86个
1.该资源内容由用户上传,如若侵权请联系客服进行举报
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
版权申诉
0 下载量 168 浏览量
2024-03-21
11:28:18
上传
评论
收藏 93.1MB ZIP 举报
温馨提示
基于yolov5的扑克牌识别项目源码+数据集+模型
资源推荐
资源详情
资源评论
收起资源包目录
基于yolov5的扑克牌识别项目源码+数据集+模型 (434个子文件)
build.bat 469B
setup.cfg 2KB
pythoncom39.dll 654KB
pywintypes39.dll 130KB
Dockerfile 2KB
Dockerfile 821B
Dockerfile-arm64 2KB
Dockerfile-cpu 2KB
.dockerignore 4KB
VC_redist.x64.exe 24.29MB
.gitattributes 75B
.gitignore 4KB
tubiao.ico 17KB
tutorial.ipynb 100KB
tutorial.ipynb 53KB
tutorial.ipynb 42KB
15.jpg 573KB
49.jpg 567KB
57.jpg 566KB
29.jpg 565KB
19.jpg 563KB
20.jpg 563KB
47.jpg 561KB
41.jpg 561KB
54.jpg 561KB
25.jpg 560KB
60.jpg 559KB
14.jpg 559KB
64.jpg 557KB
43.jpg 556KB
23.jpg 556KB
73.jpg 555KB
69.jpg 555KB
52.jpg 555KB
1.jpg 551KB
5.jpg 548KB
144.jpg 548KB
31.jpg 547KB
32.jpg 547KB
55.jpg 546KB
9.jpg 544KB
58.jpg 544KB
145.jpg 543KB
140.jpg 542KB
21.jpg 541KB
66.jpg 540KB
68.jpg 539KB
59.jpg 538KB
155.jpg 538KB
38.jpg 536KB
168.jpg 535KB
138.jpg 535KB
70.jpg 534KB
45.jpg 532KB
72.jpg 532KB
53.jpg 532KB
133.jpg 531KB
61.jpg 530KB
7.jpg 529KB
163.jpg 529KB
6.jpg 529KB
13.jpg 528KB
34.jpg 527KB
48.jpg 526KB
119.jpg 526KB
33.jpg 525KB
134.jpg 525KB
36.jpg 524KB
8.jpg 523KB
139.jpg 523KB
65.jpg 523KB
115.jpg 522KB
50.jpg 521KB
162.jpg 521KB
137.jpg 519KB
169.jpg 519KB
141.jpg 518KB
10.jpg 518KB
164.jpg 517KB
165.jpg 517KB
151.jpg 517KB
84.jpg 514KB
99.jpg 513KB
135.jpg 512KB
82.jpg 511KB
143.jpg 510KB
170.jpg 510KB
171.jpg 510KB
117.jpg 510KB
156.jpg 510KB
62.jpg 509KB
11.jpg 509KB
116.jpg 508KB
118.jpg 508KB
79.jpg 508KB
75.jpg 508KB
89.jpg 507KB
136.jpg 506KB
100.jpg 506KB
106.jpg 506KB
共 434 条
- 1
- 2
- 3
- 4
- 5
资源评论
程序员柳
- 粉丝: 6436
- 资源: 1379
下载权益
C知道特权
VIP文章
课程特权
开通VIP
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
最新资源
- nginx配置文件,注意其中需要对应宿主机的路径
- 自用数据集自用数据集自用数据集
- HTML5小游戏【堆雪人-优秀H5小游戏合集】游戏源码分享下载 - epicsnowman.zip
- SNMP Client 是SNMP测试工具
- Android Camera内存统计脚本
- AD9220高速数据芯片硬件参考设计原理图+STM32F103单片机驱动程序代码+芯片技术手册资料.zip
- 常用爆破用户名字典top500
- meta-llama-3-8b-instruct 的 model-00003-of-00004.safetensors 的2/3
- bootstrap-select.js bootstrap-select.css
- EasyPoi Excel和 Word简易工具类
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功