Ankle/yolov5_birdproject
1
0 Fork 0
Code Issues Pull Requests Actions 3 Packages Projects Releases Wiki Activity
Files
443ef7f33e0943ccc5e5c8ff922c6fe7a0cb7053
yolov5_birdproject/README.zh-CN.md
T

483 lines
38 KiB
Markdown
Raw Blame History

This file contains ambiguous Unicode characters
This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
<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>
[英语](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 🚀 是世界上最受欢迎的视觉 AI，代表<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>.
<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 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>
</div>
## <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>
</div>
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（小时） | 速度<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（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。<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（NMS 每张图像增加约 1 毫秒）。<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`
</details>
<details>
<summary>Segmentation Usage Examples &nbsp;<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
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
```
</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
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.
```
</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>
- [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
</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>
| 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) |
## <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>
- **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`
</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（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)<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>
- 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<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`
</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。
| 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（小时） | 速度<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（乙） |
| ------------------------------------------------------------------------------------------- | --------------- | ---------------- | -------------- | ------------------------------ | ----------------------------- | -------------------------------- | -------------- | ------------------- |
| [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 |
<details>
<summary>Table Notes (click to expand)</summary>
- 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>
<details>
<summary>Classification Usage Examples &nbsp;<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
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
```
</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>
## <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">
## <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：
- **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).
## <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
Reference in new issue View Git Blame Copy Permalink