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< div align = "center" >
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[英语 ](README.md )\|[简体中文 ](README.zh-CN.md )< br >
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< 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 >
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< br >
YOLOv5 🚀 是世界上最受欢迎的视觉 AI， < a href = "https://ultralytics.com" > 超力< / a > 对未来视觉 AI 方法的开源研究，结合在数千小时的研究和开发中积累的经验教训和最佳实践。
要申请企业许可证，请填写表格< a href = "https://ultralytics.com/license" > Ultralytics 许可< / a > .
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< / div >
## <div align="center">Ultralytics 现场会议</div>
< div align = "center" >
[Ultralytics Live Session Ep。 2个 ](https://youtu.be/LKpuzZllNpA )✨将直播**欧洲中部时间 12 月 13 日星期二 19:00**和[约瑟夫·纳尔逊](https://github.com/josephofiowa)的[机器人流](https://roboflow.com/?ref=ultralytics)谁将与我们一起讨论全新的 Roboflow x Ultralytics HUB 集成。收听 Glenn 和 Joseph 询问如何通过无缝数据集集成来加快工作流程！ 🔥
< a align = "center" href = "https://youtu.be/LKpuzZllNpA" target = "_blank" >
< img width = "800" src = "https://user-images.githubusercontent.com/85292283/205996456-bf3efa33-9c46-455e-b322-a64886cc7a0b.png" > < / a >
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## <div align="center">细分 ⭐ 新</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 >
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我们新的 YOLOv5[发布 v7.0](https://github.com/ultralytics/yolov5/releases/v7.0)实例分割模型是世界上最快和最准确的，击败所有当前[SOTA 基准](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco).我们使它们非常易于训练、验证和部署。查看我们的完整详细信息[发行说明](https://github.com/ultralytics/yolov5/releases/v7.0)并访问我们的[YOLOv5 分割 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb)快速入门教程。
< details >
< summary > Segmentation Checkpoints< / summary >
< br >
我们使用 A100 GPU 在 COCO 上以 640 图像大小训练了 300 个时期的 YOLOv5 分割模型。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。我们在 Google 上进行了所有速度测试[协作临](https://colab.research.google.com/signup)便于重现的笔记本。
| 模型 | 尺寸< br > < sup > （像素） | 地图< sup > 盒子< br > 50-95 | 地图< sup > 面具< br > 50-95 | 火车时间< br > < sup > 300个纪元< br > A100（ ） < br > < sup > ONNX 中央处理器< br > （小姐） | 速度< br > < sup > 同仁堂A100< br > （小姐） | 参数< br > < sup > (男) | 失败者< br > < sup > @640（ ）
| ------------------------------------------------------------------------------------------ | --------------- | ------------------ | ------------------ | ------------------------------- | ----------------------------- | -------------------------- | -------------- | ------------------- |
| [YOLOv5n-se ](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-se ](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段 ](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-se ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt ) | 640 | 49.0 | 39.9 | 我： （ ）
| [YOLOv5x-seg ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt ) | 640 | **50.7** | **41.4** | 62:56 (zks) | 1579.2 | 4.5 | 88.8 | 265.7 |
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- 使用 SGD 优化器将所有检查点训练到 300 个时期`lr0=0.01`和`weight_decay=5e-5`在图像大小 640 和所有默认设置。< br > 运行记录到[HTTPS://玩豆瓣.爱/Glenn-就ocher/yo lo V5_V70_official](https://wandb.ai/glenn-jocher/YOLOv5_v70_official)
- ** 准确性**值适用于 COCO 数据集上的单模型单尺度。< br > 重现者`python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
- ** 速度**使用 a 对超过 100 个推理图像进行平均[协作临](https://colab.research.google.com/signup)A100 高 RAM 实例。值仅表示推理速度（ < br > 重现者`python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`
- ** 出口**到 FP32 的 ONNX 和 FP16 的 TensorRT 完成`export.py`.< br > 重现者`python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`
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< / details >
< details >
< summary > Segmentation Usage Examples < a href = "https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb" > < img src = "https://colab.research.google.com/assets/colab-badge.svg" alt = "Open In Colab" > < / a > < / summary >
### 火车
YOLOv5分割训练支持自动下载COCO128-seg分割数据集`--data coco128-seg.yaml`COCO-segments 数据集的参数和手动下载`bash data/scripts/get_coco.sh --train --val --segments`接着`python train.py --data coco.yaml`.
```bash
# Single-GPU
python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640
# Multi-GPU DDP
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3
```
### 瓦尔
在 COCO 数据集上验证 YOLOv5s-seg mask mAP：
```bash
bash data/scripts/get_coco.sh --val --segments # download COCO val segments split (780MB, 5000 images)
python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate
```
### 预测
使用预训练的 YOLOv5m-seg.pt 来预测 bus.jpg：
```bash
python segment/predict.py --weights yolov5m-seg.pt --data data/images/bus.jpg
```
```python
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model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5m-seg.pt') # load from PyTorch Hub (WARNING: inference not yet supported)
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```
|  |  |
| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
### 出口
将 YOLOv5s-seg 模型导出到 ONNX 和 TensorRT：
```bash
python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
```
< / details >
## <div align="center">文档</div>
见[YOLOv5 文档](https://docs.ultralytics.com)有关培训、测试和部署的完整文档。请参阅下面的快速入门示例。
< details open >
< summary > Install< / summary >
克隆回购并安装[要求.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt)在一个[**Python>=3.7.0**](https://www.python.org/)环境，包括[**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 >
< summary > Inference< / summary >
YOLOv5[PyTorch 中心](https://github.com/ultralytics/yolov5/issues/36)推理。[楷模](https://github.com/ultralytics/yolov5/tree/master/models)自动从最新下载
YOLOv5[发布](https://github.com/ultralytics/yolov5/releases).
```python
import torch
# Model
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5n - yolov5x6, custom
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# Images
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img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list
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# Inference
results = model(img)
# Results
results.print() # or .show(), .save(), .crop(), .pandas(), etc.
```
< / details >
< details >
< summary > Inference with detect.py< / summary >
`detect.py` 在各种来源上运行推理，下载[楷模](https://github.com/ultralytics/yolov5/tree/master/models)自动从
最新的YOLOv5[发布](https://github.com/ultralytics/yolov5/releases)并将结果保存到`runs/detect`.
```bash
python detect.py --weights yolov5s.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
list.txt # list of images
list.streams # list of streams
'path/*.jpg' # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
```
< / details >
< details >
< summary > Training< / summary >
下面的命令重现 YOLOv5[可可](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)结果。[楷模](https://github.com/ultralytics/yolov5/tree/master/models)和[数据集](https://github.com/ultralytics/yolov5/tree/master/data)自动从最新下载
YOLOv5[发布](https://github.com/ultralytics/yolov5/releases). YOLOv5n/s/m/l/x 的训练时间为
V100 GPU 上 1/2/4/6/8 天（[多GPU](https://github.com/ultralytics/yolov5/issues/475)倍快）。使用
最大的`--batch-size`可能，或通过`--batch-size -1`为了
YOLOv5[自动批处理](https://github.com/ultralytics/yolov5/pull/5092).显示的批量大小适用于 V100-16GB。
```bash
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --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 >
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- [训练自定义数据 ](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data )🚀 推荐
- [获得最佳训练结果的技巧 ](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results )☘️
推荐的
- [多 GPU 训练 ](https://github.com/ultralytics/yolov5/issues/475 )
- [PyTorch 中心 ](https://github.com/ultralytics/yolov5/issues/36 )🌟 新
- [TFLite、ONNX、CoreML、TensorRT 导出 ](https://github.com/ultralytics/yolov5/issues/251 )🚀
- [NVIDIA Jetson Nano 部署 ](https://github.com/ultralytics/yolov5/issues/9627 )🌟 新
- [测试时间增强 (TTA) ](https://github.com/ultralytics/yolov5/issues/303 )
- [模型集成 ](https://github.com/ultralytics/yolov5/issues/318 )
- [模型修剪/稀疏度 ](https://github.com/ultralytics/yolov5/issues/304 )
- [超参数进化 ](https://github.com/ultralytics/yolov5/issues/607 )
- [使用冻结层进行迁移学习 ](https://github.com/ultralytics/yolov5/issues/1314 )
- [架构总结 ](https://github.com/ultralytics/yolov5/issues/6998 )🌟 新
- [用于数据集、标签和主动学习的 Roboflow ](https://github.com/ultralytics/yolov5/issues/4975 )🌟 新
- [ClearML 记录 ](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml )🌟 新
- [所以平台 ](https://github.com/ultralytics/yolov5/wiki/Deci-Platform )🌟 新
- [彗星记录 ](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet )🌟 新
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< / details >
## <div align="center">集成</div>
< br >
< a align = "center" href = "https://bit.ly/ultralytics_hub" target = "_blank" >
< img width = "100%" src = "https://github.com/ultralytics/assets/raw/master/im/integrations-loop.png" > < / a >
< br >
< br >
< div align = "center" >
< a href = "https://roboflow.com/?ref=ultralytics" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "15%" height = "0" alt = "" / >
< a href = "https://cutt.ly/yolov5-readme-clearml" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-clearml.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "15%" height = "0" alt = "" / >
< a href = "https://bit.ly/yolov5-readme-comet" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-comet.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "15%" height = "0" alt = "" / >
< a href = "https://bit.ly/yolov5-deci-platform" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-deci.png" width = "10%" / > < / a >
< / div >
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| 机器人流 | ClearML ⭐ 新 | 彗星⭐新 | 所以⭐新 |
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| :-------------------------------------------------------------------------: | :-----------------------------------------------------------------------: | :----------------------------------------------------------------------------: | :---------------------------------------------------------------: |
| 将您的自定义数据集标记并直接导出到 YOLOv5 以进行训练[机器人流](https://roboflow.com/?ref=ultralytics) | 使用自动跟踪、可视化甚至远程训练 YOLOv5[清除ML](https://cutt.ly/yolov5-readme-clearml)（开源！） | 永远免费，[彗星](https://bit.ly/yolov5-readme-comet)可让您保存 YOLOv5 模型、恢复训练以及交互式可视化和调试预测 | 一键自动编译量化YOLOv5以获得更好的推理性能[所以](https://bit.ly/yolov5-deci-platform) |
## <div align="center">Ultralytics 集线器</div>
[Ultralytics 集线器 ](https://bit.ly/ultralytics_hub )是我们的⭐**新的**用于可视化数据集、训练 YOLOv5 🚀 模型并以无缝体验部署到现实世界的无代码解决方案。开始使用**自由的**现在！
< a align = "center" href = "https://bit.ly/ultralytics_hub" target = "_blank" >
< img width = "100%" src = "https://github.com/ultralytics/assets/raw/master/im/ultralytics-hub.png" > < / a >
## <div align="center">为什么选择 YOLOv5</div>
YOLOv5 被设计为超级容易上手和简单易学。我们优先考虑现实世界的结果。
< p align = "left" > < img width = "800" src = "https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png" > < / p >
< details >
< summary > YOLOv5-P5 640 Figure< / summary >
< p align = "left" > < img width = "800" src = "https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png" > < / p >
< / details >
< details >
< summary > Figure Notes< / summary >
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- **COCO AP 值** 表示[map@0.5](mailto:mAP@0.5):0.95 指标在 5000 张图像上测得[COCO val2017](http://cocodataset.org)从 256 到 1536 的各种推理大小的数据集。
- ** 显卡速度**测量每张图像的平均推理时间[COCO val2017](http://cocodataset.org)数据集使用[美国销售.Excelerge](https://aws.amazon.com/ec2/instance-types/p3/)批量大小为 32 的 V100 实例。
- ** 高效**数据来自[谷歌/汽车](https://github.com/google/automl)批量大小为 8。
- ** 复制**经过`python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
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< / details >
### 预训练检查点
| 模型 | 尺寸< br > < sup > （像素） | 地图< sup > 值< br > 50-95 | 地图< sup > 值< br > 50 | 速度< br > < sup > 处理器b1< br > （小姐） | 速度< br > < sup > V100 b1< br > （小姐） | 速度< br > < sup > V100 b32< br > （小姐） | 参数< br > < sup > (男) | 失败者< br > < sup > @640（ ）
| --------------------------------------------------------------------------------------------------- | --------------- | ----------------- | ---------------- | ------------------------ | -------------------------- | --------------------------- | -------------- | ------------------- |
| [YOLOv5n ](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n.pt ) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
| [YOLOv5s ](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt ) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
| [YOLOv5m ](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m.pt ) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
| [YOLOv5l ](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l.pt ) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
| [YOLOv5x ](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x.pt ) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
| | | | | | | | | |
| [YOLOv5n6 ](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n6.pt ) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
| [YOLOv5s6 ](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s6.pt ) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
| [YOLOv5m6 ](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m6.pt ) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
| [YOLOv5l6 ](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l6.pt ) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
| [YOLOv5x6 ](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x6.pt )< br > +[电讯局][tta] | 1280< br > 1536 | 55.0< br > **55.8** | 72.7< br > **72.7** | 3136< br > - | 26.2< br > - | 19.4< br > - | 140.7< br > - | 209.8< br > - |
< details >
< summary > Table Notes< / summary >
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- 所有检查点都使用默认设置训练到 300 个时期。纳米和小型型号使用[hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml)hyps，
- ** 地图< sup > 值</ sup > **值适用于单模型单尺度[COCO val2017](http://cocodataset.org)数据集。< br > 重现者`python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
- ** 速度**使用 a 对 COCO val 图像进行平均[美国销售.Excelerge](https://aws.amazon.com/ec2/instance-types/p3/)实例。 NMS 时间 (~1 ms/img) 不包括在内。< br > 重现者`python val.py --data coco.yaml --img 640 --task speed --batch 1`
- ** 电讯局**[测试时间增加](https://github.com/ultralytics/yolov5/issues/303)包括反射和尺度增强。< br > 重现者`python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
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< / details >
## <div align="center">分类⭐新</div>
YOLOv5[发布 v6.2](https://github.com/ultralytics/yolov5/releases)带来对分类模型训练、验证和部署的支持！查看我们的完整详细信息[发行说明](https://github.com/ultralytics/yolov5/releases/v6.2)并访问我们的[YOLOv5 分类 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb)快速入门教程。
< details >
< summary > Classification Checkpoints< / summary >
< br >
我们使用 4xA100 实例在 ImageNet 上训练了 90 个时期的 YOLOv5-cls 分类模型，我们训练了 ResNet 和 EfficientNet 模型以及相同的默认训练设置以进行比较。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。我们在 Google 上进行了所有速度测试[协作临](https://colab.research.google.com/signup)为了便于重现。
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| 模型 | 尺寸< br > < sup > （像素） | acc< br > < sup > top1 | acc< br > < sup > 烹饪 | 训练< br > < sup > 90个纪元< br > 4xA100（ ） < br > < sup > ONNX 中央处理器< br > （小姐） | 速度< br > < sup > TensorRT V100< br > （小姐） | 参数< br > < sup > (男) | 失败者< br > < sup > @224（ ）
| ------------------------------------------------------------------------------------------- | --------------- | ---------------- | -------------- | ------------------------------ | ----------------------------- | -------------------------------- | -------------- | ------------------- |
| [YOLOv5n-cls ](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n-cls.pt ) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** |
| [YOLOv5s-cls ](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s-cls.pt ) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |
| [YOLOv5m-cls ](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m-cls.pt ) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |
| [YOLOv5l-cls ](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l-cls.pt ) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |
| [YOLOv5x-cls ](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x-cls.pt ) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |
| | | | | | | | | |
| [ResNet18 ](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet18.pt ) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 |
| [Resnetzch ](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet34.pt ) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |
| [ResNet50 ](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet50.pt ) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |
| [ResNet101 ](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet101.pt ) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |
| | | | | | | | | |
| [高效网络\_b0 ](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b0.pt ) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |
| [高效网络 b1 ](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b1.pt ) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |
| [我们将预测 ](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b2.pt ) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |
| [高效Netb3 ](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b3.pt ) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
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< details >
< summary > Table Notes (click to expand)< / summary >
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- 使用 SGD 优化器将所有检查点训练到 90 个时期`lr0=0.001`和`weight_decay=5e-5`在图像大小 224 和所有默认设置。< br > 运行记录到[HTTPS://玩豆瓣.爱/Glenn-就ocher/yo lo V5-classifier-V6-2](https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2)
- ** 准确性**值适用于单模型单尺度[ImageNet-1k](https://www.image-net.org/index.php)数据集。< br > 重现者`python classify/val.py --data ../datasets/imagenet --img 224`
- ** 速度**使用谷歌平均超过 100 个推理图像[协作临](https://colab.research.google.com/signup)V100 高 RAM 实例。< br > 重现者`python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
- ** 出口**到 FP32 的 ONNX 和 FP16 的 TensorRT 完成`export.py`.< br > 重现者`python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
< / details >
< / details >
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< details >
< summary > Classification Usage Examples < a href = "https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb" > < img src = "https://colab.research.google.com/assets/colab-badge.svg" alt = "Open In Colab" > < / a > < / summary >
### 火车
YOLOv5 分类训练支持自动下载 MNIST、Fashion-MNIST、CIFAR10、CIFAR100、Imagenette、Imagewoof 和 ImageNet 数据集`--data`争论。开始使用 MNIST 进行训练`--data mnist`.
```bash
# Single-GPU
python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
# Multi-GPU DDP
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
```
### 瓦尔
在 ImageNet-1k 数据集上验证 YOLOv5m-cls 的准确性：
```bash
bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)
python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate
```
### 预测
使用预训练的 YOLOv5s-cls.pt 来预测 bus.jpg：
```bash
python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
```
```python
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model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt') # load from PyTorch Hub
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```
### 出口
将一组经过训练的 YOLOv5s-cls、ResNet 和 EfficientNet 模型导出到 ONNX 和 TensorRT：
```bash
python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
```
< / details >
## <div align="center">环境</div>
在几秒钟内开始使用我们经过验证的环境。单击下面的每个图标了解详细信息。
< div align = "center" >
< a href = "https://bit.ly/yolov5-paperspace-notebook" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gradient.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "5%" alt = "" / >
< a href = "https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "5%" alt = "" / >
< a href = "https://www.kaggle.com/ultralytics/yolov5" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "5%" alt = "" / >
< a href = "https://hub.docker.com/r/ultralytics/yolov5" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "5%" alt = "" / >
< a href = "https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "5%" alt = "" / >
< a href = "https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width = "10%" / > < / a >
< / div >
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## <div align="center">应用程序</div>
在您的 iOS 或 Android 设备上运行 YOLOv5 模型[Ultralytics 应用程序](https://ultralytics.com/app_install)!
< a align = "center" href = "https://ultralytics.com/app_install" target = "_blank" >
< img width = "100%" alt = "Ultralytics mobile app" src = "https://user-images.githubusercontent.com/26833433/202829285-39367043-292a-41eb-bb76-c3e74f38e38e.png" >
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## <div align="center">贡献</div>
我们喜欢您的意见！我们希望尽可能简单和透明地为 YOLOv5 做出贡献。请看我们的[投稿指南](CONTRIBUTING.md)开始，并填写[YOLOv5调查](https://ultralytics.com/survey?utm_source=github& utm_medium=social& utm_campaign=Survey)向我们发送您的体验反馈。感谢我们所有的贡献者！
<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
< a href = "https://github.com/ultralytics/yolov5/graphs/contributors" > < img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/image-contributors-1280.png" / > < / a >
## <div align="center">执照</div>
YOLOv5 在两种不同的许可下可用：
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- **GPL-3.0 许可证** ：
- ** 企业执照**：在没有 GPL-3.0 开源要求的情况下为商业产品开发提供更大的灵活性。典型用例是将 Ultralytics 软件和 AI 模型嵌入到商业产品和应用程序中。在以下位置申请企业许可证[Ultralytics 许可](https://ultralytics.com/license).
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## <div align="center">接触</div>
对于 YOLOv5 错误和功能请求，请访问[GitHub 问题](https://github.com/ultralytics/yolov5/issues).如需专业支持，请[联系我们](https://ultralytics.com/contact).
< br >
< 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 = "3%" alt = "" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "3%" 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 = "3%" alt = "" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "3%" 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 = "3%" alt = "" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "3%" 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 = "3%" alt = "" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "3%" 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 = "3%" alt = "" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "3%" 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 = "3%" alt = "" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "3%" 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 = "3%" alt = "" / > < / a >
< / div >
[tta]: https://github.com/ultralytics/yolov5/issues/303
