<div align="center">
<p>
<a align="left" href="https://ultralytics.com/yolov5" target="_blank">
<img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a>
</p>
<br>
<div>
<a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></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://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>
<a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a>
</div>
<br>
<p>
YOLOv5 ð is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a>
open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
</p>
<div align="center">
<a href="https://github.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.linkedin.com/company/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://twitter.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.producthunt.com/@glenn_jocher">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://youtube.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.facebook.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.instagram.com/ultralytics/">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="2%"/>
</a>
</div>
<!--
<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
<img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a>
-->
</div>
## <div align="center">Documentation</div>
See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
## <div align="center">Quick Start Examples</div>
<details open>
<summary>Install</summary>
Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a
[**Python>=3.7.0**](https://www.python.org/) environment, including
[**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 open>
<summary>Inference</summary>
Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)
. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest
YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
```python
import torch
# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, 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` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from
the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
```bash
python detect.py --source 0 # webcam
img.jpg # image
vid.mp4 # video
path/ # directory
path/*.jpg # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
```
</details>
<details>
<summary>Training</summary>
The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
results. [Models](https://github.com/ultralytics/yolov5/tree/master/models)
and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest
YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are
1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the
largest `--batch-size` possible, or pass `--batch-size -1` for
YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
```bash
python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --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>
* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) ð RECOMMENDED
* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) âï¸
RECOMMENDED
* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) ð NEW
* [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975) ð NEW
* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) â NEW
* [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) ð
* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
* [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) â NEW
* [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998) â NEW
</details>
## <div align="center">Environments</div>
Get started in seconds with our verified environments. Click each icon below for details.
<div align="center">
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
<img src="https://github
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
使用yolov5进行摔倒检测,文件包含项目所需的所有文件,也包括环境安装文件,包含已训练好的模型权重文件,包含官方的detect文件和自写的demo,运行demo_person_fall.py即可,可自行更改路径识别图片和视频
资源推荐
资源详情
资源评论
收起资源包目录
yolov5 摔倒检测 跌倒识别 (308个子文件)
output.avi 10.7MB
setup.cfg 1KB
Dockerfile 2KB
Dockerfile 821B
.dockerignore 4KB
.DS_Store 14KB
.DS_Store 10KB
.DS_Store 10KB
.DS_Store 10KB
.DS_Store 8KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
.gitattributes 75B
.gitignore 4KB
tutorial.ipynb 55KB
ad3ec6fe95280cbe97bd7e6793b4b92e.jpeg 1.19MB
dc9ebf3bf1eff783d387375151720b81.jpeg 387KB
bcd45c5f7c88d61a0ecdc6fba333ae27.jpeg 278KB
1bf883d3b65df22b5e79dddd7041bb53.jpeg 160KB
fce5ad0879f1bc2957b9637db8bcafea.jpeg 121KB
c7cdf7ab65f0fa3eec2409683f8cfccc.jpeg 90KB
844bce70f3af6f75f6dc9680af7bac3c.jpeg 51KB
a6a90124e5e2959cc61b18956b1eac2b.jpeg 48KB
49db0a18f3a1ceed0c3b0e5a6590d852.jpeg 47KB
670ff0aa27daf439424581ecfa438e6f.jpeg 39KB
ca2094579bd004a02add06599a9db85e.jpeg 38KB
ebike_9.jpg 130KB
ebike_0.jpg 68KB
ebike_4.jpg 36KB
ebike_5.jpg 29KB
ebike_7.jpg 26KB
ebike_8.jpg 24KB
ebike_10.jpg 22KB
ebike_6.jpg 19KB
ebike_2.jpg 16KB
ebike_1.jpg 15KB
ebike_3.jpg 14KB
wandb-metadata.json 872B
wandb-metadata.json 870B
wandb-metadata.json 870B
wandb-metadata.json 835B
wandb-metadata.json 835B
wandb-metadata.json 835B
wandb-metadata.json 835B
wandb-summary.json 27B
wandb-summary.json 27B
wandb-summary.json 26B
wandb-summary.json 26B
wandb-summary.json 26B
wandb-summary.json 26B
wandb-summary.json 2B
latest-run 28B
LICENSE 34KB
debug-internal.log 21KB
debug-internal.log 21KB
debug-internal.log 21KB
debug-internal.log 21KB
debug-internal.log 20KB
debug-internal.log 14KB
output.log 12KB
debug.log 10KB
debug.log 9KB
debug.log 9KB
output.log 9KB
debug.log 9KB
debug-internal.log 9KB
output.log 9KB
output.log 7KB
output.log 7KB
output.log 7KB
debug.log 6KB
debug.log 5KB
debug.log 5KB
output.log 985B
debug-internal.log 52B
debug.log 43B
debug-cli.chenzihao.log 0B
README.md 15KB
README.md 11KB
CONTRIBUTING.md 5KB
共 308 条
- 1
- 2
- 3
- 4
Carry陈
- 粉丝: 513
- 资源: 6
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功
- 1
- 2
- 3
- 4
- 5
- 6
前往页