From 1607aec4312719db820a026792223acad915015f Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Thu, 8 Dec 2022 17:27:36 -0800 Subject: [PATCH] Automatic README translation to Simplified Chinese (#10445) * Create translate-readme.yml @AyushExel @pderrenger @Laughing-q adding README translation action since we are unable to manually maintain our Chinese-translated README Signed-off-by: Glenn Jocher * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update README.md Signed-off-by: Glenn Jocher * Double hyperlinks Signed-off-by: Glenn Jocher * Delete README_cn.md Signed-off-by: Glenn Jocher * Create README.zh-CN.md Signed-off-by: Glenn Jocher * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update README.md Signed-off-by: Glenn Jocher * Update .pre-commit-config.yaml Signed-off-by: Glenn Jocher Signed-off-by: Glenn Jocher Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .github/README_cn.md | 344 ------------------ .github/workflows/translate-readme.yml | 27 ++ .pre-commit-config.yaml | 2 +- README.md | 17 +- README.zh-CN.md | 479 +++++++++++++++++++++++++ 5 files changed, 513 insertions(+), 356 deletions(-) delete mode 100644 .github/README_cn.md create mode 100644 .github/workflows/translate-readme.yml create mode 100644 README.zh-CN.md diff --git a/.github/README_cn.md b/.github/README_cn.md deleted file mode 100644 index 0a2f61e..0000000 --- a/.github/README_cn.md +++ /dev/null @@ -1,344 +0,0 @@ -
-

- - -

- - [English](../README.md) | 简体中文 -
-
- YOLOv5 CI - YOLOv5 Citation - Docker Pulls -
- Run on Gradient - Open In Colab - Open In Kaggle -
- -
-

- YOLOv5🚀是一个在COCO数据集上预训练的物体检测架构和模型系列，它代表了Ultralytics对未来视觉AI方法的公开研究，其中包含了在数千小时的研究和开发中所获得的经验和最佳实践。 -

- -
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-
- - -##
文件
- -请参阅[YOLOv5 Docs](https://docs.ultralytics.com)，了解有关训练、测试和部署的完整文件。 - -##
快速开始案例
- -
-安装 - -在[**Python>=3.7.0**](https://www.python.org/) 的环境中克隆版本仓并安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt)，包括[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/)。 -```bash -git clone https://github.com/ultralytics/yolov5 # 克隆 -cd yolov5 -pip install -r requirements.txt # 安装 -``` - -
- -
-推理 - -YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 推理. [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从最新YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases)下载。 - -```python -import torch - -# 模型 -model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5n - yolov5x6, custom - -# 图像 -img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list - -# 推理 -results = model(img) - -# 结果 -results.print() # or .show(), .save(), .crop(), .pandas(), etc. -``` - -
- -
-用 detect.py 进行推理 - -`detect.py` 在各种数据源上运行推理, 其会从最新的 YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases) 中自动下载 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 并将检测结果保存到 `runs/detect` 目录。 - -```bash -python detect.py --source 0 # 网络摄像头 - img.jpg # 图像 - vid.mp4 # 视频 - path/ # 文件夹 - 'path/*.jpg' # glob - 'https://youtu.be/Zgi9g1ksQHc' # YouTube - 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP 流 -``` - -
- -
-训练 - -以下指令再现了 YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) -数据集结果. [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) 自动从最新的YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases) 中下载。YOLOv5n/s/m/l/x的训练时间在V100 GPU上是 1/2/4/6/8天（多GPU倍速）. 尽可能使用最大的 `--batch-size`, 或通过 `--batch-size -1` 来实现 YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092). 批量大小显示为 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 -``` - - - -
- -
-教程 - -- [训练自定义数据集](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 推荐 -- [获得最佳训练效果的技巧](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️ - 推荐 -- [多GPU训练](https://github.com/ultralytics/yolov5/issues/475) -- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 🌟 新 -- [TFLite, ONNX, CoreML, TensorRT 输出](https://github.com/ultralytics/yolov5/issues/251) 🚀 -- [测试时数据增强 (TTA)](https://github.com/ultralytics/yolov5/issues/303) -- [模型集成](https://github.com/ultralytics/yolov5/issues/318) -- [模型剪枝/稀疏性](https://github.com/ultralytics/yolov5/issues/304) -- [超参数进化](https://github.com/ultralytics/yolov5/issues/607) -- [带有冻结层的迁移学习](https://github.com/ultralytics/yolov5/issues/1314) -- [架构概要](https://github.com/ultralytics/yolov5/issues/6998) 🌟 新 -- [使用Weights & Biases 记录实验](https://github.com/ultralytics/yolov5/issues/1289) -- [Roboflow：数据集，标签和主动学习](https://github.com/ultralytics/yolov5/issues/4975) 🌟 新 -- [使用ClearML 记录实验](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) 🌟 新 -- [Deci 平台](https://github.com/ultralytics/yolov5/wiki/Deci-Platform) 🌟 新 - -
- - -##
Integrations
- -
- - -
-
- -
- - - - - - - - - - - -
- -|Roboflow|ClearML ⭐ NEW|Comet ⭐ NEW|Deci ⭐ NEW| -|:-:|:-:|:-:|:-:| -|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics)|Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!)|Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLOv5 models, resume training, and interactively visualise and debug predictions|Automatically compile and quantize YOLOv5 for better inference performance in one click at [Deci](https://bit.ly/yolov5-deci-platform)| - - -##
Ultralytics HUB
- -[Ultralytics HUB](https://bit.ly/ultralytics_hub) is our ⭐ **NEW** no-code solution to visualize datasets, train YOLOv5 🚀 models, and deploy to the real world in a seamless experience. Get started for **Free** now! - - - - - -##
为什么选择 YOLOv5
- -

-
- YOLOv5-P5 640 图像 (点击扩展) - -

-
-
- 图片注释 (点击扩展) - -- **COCO AP val** 表示 mAP@0.5:0.95 在5000张图像的[COCO val2017](http://cocodataset.org)数据集上，在256到1536的不同推理大小上测量的指标。 -- **GPU Speed** 衡量的是在 [COCO val2017](http://cocodataset.org) 数据集上使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100实例在批量大小为32时每张图像的平均推理时间。 -- **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) ，批量大小设置为 8。 -- 复现 mAP 方法: `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` - -
- -### 预训练检查点 - -| 模型 | 规模
(像素) | mAP验证
0.5:0.95 | mAP验证
0.5 | 速度
CPU b1
(ms) | 速度
V100 b1
(ms) | 速度
V100 b32
(ms) | 参数
(M) | 浮点运算
@640 (B) | -|------------------------------------------------------------------------------------------------------|-----------------------|-------------------------|--------------------|------------------------------|-------------------------------|--------------------------------|--------------------|------------------------| -| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** | -| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 | -| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 | -| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 | -| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 | -| | | | | | | | | | -| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 | -| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 | -| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 | -| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 | -| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x6.pt)
+ [TTA][TTA] | 1280
1536 | 55.0
**55.8** | 72.7
**72.7** | 3136
- | 26.2
- | 19.4
- | 140.7
- | 209.8
- | - -
- 表格注释 (点击扩展) - -- 所有检查点都以默认设置训练到300个时期. Nano和Small模型用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, 其他模型使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml). -- **mAPval** 值是 [COCO val2017](http://cocodataset.org) 数据集上的单模型单尺度的值。 -
复现方法: `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` -- 使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) 实例对COCO val图像的平均速度。不包括NMS时间（~1 ms/img) -
复现方法: `python val.py --data coco.yaml --img 640 --task speed --batch 1` -- **TTA** [测试时数据增强](https://github.com/ultralytics/yolov5/issues/303) 包括反射和比例增强. -
复现方法: `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` - -
- - -##
分类 ⭐ 新
- -YOLOv5发布的[v6.2版本](https://github.com/ultralytics/yolov5/releases) 支持训练，验证，预测和输出分类模型！这使得训练分类器模型非常简单。点击下面开始尝试！ - -
- 分类检查点 (点击展开) - -
- -我们在ImageNet上使用了4xA100的实例训练YOLOv5-cls分类模型90个epochs，并以相同的默认设置同时训练了ResNet和EfficientNet模型来进行比较。我们将所有的模型导出到ONNX FP32进行CPU速度测试，又导出到TensorRT FP16进行GPU速度测试。最后，为了方便重现，我们在[Google Colab Pro](https://colab.research.google.com/signup)上进行了所有的速度测试。 - -| 模型 | 规模
(像素) | 准确度
第一 | 准确度
前五 | 训练
90 epochs
4xA100 (小时) | 速度
ONNX CPU
(ms) | 速度
TensorRT V100
(ms) | 参数
(M) | 浮点运算
@224 (B) | -|----------------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|----------------------------------------------|--------------------------------|-------------------------------------|--------------------|------------------------| -| [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 | -| [ResNet34](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 | -| | -| [EfficientNet_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 | -| [EfficientNet_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 | -| [EfficientNet_b2](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 | -| [EfficientNet_b3](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 | - -
- 表格注释 (点击扩展) - -- 所有检查点都被SGD优化器训练到90 epochs, `lr0=0.001` 和 `weight_decay=5e-5`， 图像大小为224，全为默认设置。
运行数据记录于 https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2。 -- **准确度** 值为[ImageNet-1k](https://www.image-net.org/index.php)数据集上的单模型单尺度。
通过`python classify/val.py --data ../datasets/imagenet --img 224`进行复制。 -- 使用Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM实例得出的100张推理图像的平均**速度**。
通过 `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`进行复制。 -- 用`export.py`**导出**到FP32的ONNX和FP16的TensorRT。
通过 `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`进行复制。 -
-
- -
- 分类使用实例 (点击展开) - -### 训练 -YOLOv5分类训练支持自动下载MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof和ImageNet数据集，并使用`--data` 参数. 打个比方，在MNIST上使用`--data mnist`开始训练。 - -```bash -# 单GPU -python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128 - -# 多-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 -``` - -### 验证 -在ImageNet-1k数据集上验证YOLOv5m-cl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 -用提前训练好的YOLOv5s-cls.pt去预测bus.jpg: -```bash -python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg -``` -```python -model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt') # load from PyTorch Hub -``` - -### 导出 -导出一组训练好的YOLOv5s-cls, ResNet和EfficientNet模型到ONNX和TensorRT: -```bash -python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224 -``` -
- - -##
贡献
- -我们重视您的意见! 我们希望给大家提供尽可能的简单和透明的方式对 YOLOv5 做出贡献。开始之前请先点击并查看我们的 [贡献指南](CONTRIBUTING.md)，填写[YOLOv5调查问卷](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 来向我们发送您的经验反馈。真诚感谢我们所有的贡献者! - - - - -##
联系
- -关于YOLOv5的漏洞和功能问题，请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues)。商业咨询或技术支持服务请访问[https://ultralytics.com/contact](https://ultralytics.com/contact)。 - -
-
- - - - - - - - - - - - - - - - - - - - -
- -[assets]: https://github.com/ultralytics/yolov5/releases -[tta]: https://github.com/ultralytics/yolov5/issues/303 diff --git a/.github/workflows/translate-readme.yml b/.github/workflows/translate-readme.yml new file mode 100644 index 0000000..76f59b8 --- /dev/null +++ b/.github/workflows/translate-readme.yml @@ -0,0 +1,27 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# README translation action to translate README.md to Chinese as README.zh-CN.md on any change to README.md + +name: Translate README + +on: + push: + branches: + - main + - master + paths: + - README.md + +jobs: + Translate: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - name: Setup Node.js + uses: actions/setup-node@v3 + with: + node-version: 16 + # ISO Langusge Codes: https://cloud.google.com/translate/docs/languages + - name: Adding README - Chinese Simplified + uses: dephraiim/translate-readme@main + with: + LANG: zh-CN diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 72c3cc6..28dbc89 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -50,7 +50,7 @@ repos: additional_dependencies: - mdformat-gfm - mdformat-black - exclude: "README.md|README_cn.md" + exclude: "README.md|README.zh-CN.md" - repo: https://github.com/asottile/yesqa rev: v1.4.0 diff --git a/README.md b/README.md index f00cb76..9ee9732 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@

- English | [简体中文](.github/README_cn.md) + [English](README.md) | [简体中文](README.zh-CN.md)
YOLOv5 CI @@ -15,15 +15,11 @@ Open In Colab Open In Kaggle
-
-

- YOLOv5 🚀 is the world's most loved vision AI, representing Ultralytics - 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 Ultralytics Licensing. -

-

+ +YOLOv5 🚀 is the world's most loved vision AI, representing Ultralytics 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 Ultralytics Licensing.
@@ -313,7 +309,7 @@ YOLOv5 has been designed to be super easy to get started and simple to learn. We | [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)
+ [TTA][TTA] | 1280
1536 | 55.0
**55.8** | 72.7
**72.7** | 3136
- | 26.2
- | 19.4
- | 140.7
- | 209.8
- | +| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x6.pt)
+ [TTA][tta] | 1280
1536 | 55.0
**55.8** | 72.7
**72.7** | 3136
- | 26.2
- | 19.4
- | 140.7
- | 209.8
- |
Table Notes @@ -479,5 +475,4 @@ For YOLOv5 bugs and feature requests please visit [GitHub Issues](https://github
-[assets]: https://github.com/ultralytics/yolov5/releases [tta]: https://github.com/ultralytics/yolov5/issues/303 diff --git a/README.zh-CN.md b/README.zh-CN.md new file mode 100644 index 0000000..09cfc94 --- /dev/null +++ b/README.zh-CN.md @@ -0,0 +1,479 @@ +
+

+ + +

+ +[英语](README.md)|[简体中文](README.zh-CN.md)
+ +
+ YOLOv5 CI + YOLOv5 Citation + Docker Pulls +
+ Run on Gradient + Open In Colab + Open In Kaggle +
+
+ +YOLOv5 🚀 是世界上最受欢迎的视觉 AI，代表超力对未来视觉 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Ultralytics 许可. + +
+ + + + + + + + + + + + + + + + + + + + +
+
+ +##
Ultralytics 现场会议
+ +
+ +[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！ 🔥 + + + +
+ +##
细分 ⭐ 新
+ +
+ + +
+ +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。 + +
+ Segmentation Checkpoints + +
+ +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
（像素） | 地图盒子
50-95 | 地图面具
50-95 | 火车时间
300个纪元
A100（小时） | 速度
ONNX 中央处理器
（小姐） | 速度
同仁堂A100
（小姐） | 参数
(男) | 失败者
@640（二） | +| ------------------------------------------------------------------------------------------ | --------------- | ------------------ | ------------------ | ------------------------------- | ----------------------------- | -------------------------- | -------------- | ------------------- | +| [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：43（X） | 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 (zks) | 1579.2 | 4.5 | 88.8 | 265.7 | + +- 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。
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。
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（NMS 每张图像增加约 1 毫秒）。
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`.
重现者`python export.py --weights yolov5s-seg.pt --include engine --device 0 --half` + +
+ +
+ Segmentation Usage Examples  Open In Colab + +### 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 +model = torch.hub.load( + "ultralytics/yolov5", "custom", "yolov5m-seg.pt" +) # load from PyTorch Hub (WARNING: inference not yet supported) +``` + +| ![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) | +| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | + +### 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 +``` + +
+ +##
文档
+ +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。 + +
+Install + +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 +``` + +
+ +
+Inference + +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 +model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, 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. +``` + +
+ +
+Inference with detect.py + +`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 +``` + +
+ +
+Training + +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 +``` + + + +
+ +
+Tutorials + +- [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 + +
+ +##
集成
+ +
+ + +
+
+ +
+ + + + + + + + + + + +
+ +| 机器人流 | ClearML ⭐ 新 | 彗星⭐新 | 所以⭐新 | +| :-------------------------------------------------------------------------: | :-----------------------------------------------------------------------: | :----------------------------------------------------------------------------: | :---------------------------------------------------------------: | +| 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) | + +##
Ultralytics 集线器
+ +[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！ + + + + +##
为什么选择 YOLOv5
+ +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。 + +

+
+ YOLOv5-P5 640 Figure + +

+
+
+ Figure Notes + +- **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` + +
+ +### 预训练检查点 + +| 模型 | 尺寸
（像素） | 地图值
50-95 | 地图值
50 | 速度
处理器b1
（小姐） | 速度
V100 b1
（小姐） | 速度
V100 b32
（小姐） | 参数
(男) | 失败者
@640（二） | +| --------------------------------------------------------------------------------------------------- | --------------- | ----------------- | ---------------- | ------------------------ | -------------------------- | --------------------------- | -------------- | ------------------- | +| [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)
+[电讯局][tta] | 1280
1536 | 55.0
**55.8** | 72.7
**72.7** | 3136
- | 26.2
- | 19.4
- | 140.7
- | 209.8
- | + +
+ Table Notes + +- 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，所有其他人都使用[hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml). +- \*\*地图值\*\*值适用于单模型单尺度[COCO val2017](http://cocodataset.org)数据集。
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。
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。
重现者`python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` + +
+ +##
分类⭐新
+ +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。 + +
+ Classification Checkpoints + +
+ +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。 + +| 模型 | 尺寸
（像素） | acc
top1 | acc
烹饪 | 训练
90个纪元
4xA100（小时） | 速度
ONNX 中央处理器
（小姐） | 速度
TensorRT V100
（小姐） | 参数
(男) | 失败者
@224（二） | +| ------------------------------------------------------------------------------------------ | --------------- | ---------------- | -------------- | ------------------------------ | ----------------------------- | -------------------------------- | -------------- | ------------------- | +| [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 | + +
+ Table Notes (click to expand) + +- 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。
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。
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。
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`.
重现者`python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224` +
+
+ +
+ Classification Usage Examples  Open In Colab + +### 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 +model = torch.hub.load( + "ultralytics/yolov5", "custom", "yolov5s-cls.pt" +) # load from PyTorch Hub +``` + +### 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 +``` + +
+ +##
环境
+ +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。 + +
+ + + + + + + + + + + + + + + + + +
+ +##
贡献
+ +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！ + + + + + +##
执照
+ +YOLOv5 在两种不同的许可下可用： + +- **GPL-3.0 许可证**： 看[执照](https://github.com/ultralytics/yolov5/blob/master/LICENSE)文件的详细信息。 +- **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). + +##
接触
+ +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). + +
+
+ + + + + + + + + + + + + + + + + + + + +
+ +[tta]: https://github.com/ultralytics/yolov5/issues/303