<a align="left" href="https://apps.apple.com/app/id1452689527" target="_blank">
<img width="800" src="https://user-images.githubusercontent.com/26833433/98699617-a1595a00-2377-11eb-8145-fc674eb9b1a7.jpg"></a>
 
<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>
This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk.
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313216-f0a5e100-9af5-11eb-8445-c682b60da2e3.png"></p>
<details>
<summary>YOLOv5-P5 640 Figure (click to expand)</summary>
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313219-f1d70e00-9af5-11eb-9973-52b1f98d321a.png"></p>
</details>
<details>
<summary>Figure Notes (click to expand)</summary>
* GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
* EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
* **Reproduce** by `python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
</details>
- **April 11, 2021**: [v5.0 release](https://github.com/ultralytics/yolov5/releases/tag/v5.0): YOLOv5-P6 1280 models, [AWS](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart), [Supervise.ly](https://github.com/ultralytics/yolov5/issues/2518) and [YouTube](https://github.com/ultralytics/yolov5/pull/2752) integrations.
- **January 5, 2021**: [v4.0 release](https://github.com/ultralytics/yolov5/releases/tag/v4.0): nn.SiLU() activations, [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme) logging, [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/) integration.
- **August 13, 2020**: [v3.0 release](https://github.com/ultralytics/yolov5/releases/tag/v3.0): nn.Hardswish() activations, data autodownload, native AMP.
- **July 23, 2020**: [v2.0 release](https://github.com/ultralytics/yolov5/releases/tag/v2.0): improved model definition, training and mAP.
## Pretrained Checkpoints
[assets]: https://github.com/ultralytics/yolov5/releases
Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPS<br><sup>640 (B)
--- |--- |--- |--- |--- |--- |---|--- |---
[YOLOv5s][assets] |640 |36.7 |36.7 |55.4 |**2.0** | |7.3 |17.0
[YOLOv5m][assets] |640 |44.5 |44.5 |63.1 |2.7 | |21.4 |51.3
[YOLOv5l][assets] |640 |48.2 |48.2 |66.9 |3.8 | |47.0 |115.4
[YOLOv5x][assets] |640 |**50.4** |**50.4** |**68.8** |6.1 | |87.7 |218.8
| | | | | | || |
[YOLOv5s6][assets] |1280 |43.3 |43.3 |61.9 |**4.3** | |12.7 |17.4
[YOLOv5m6][assets] |1280 |50.5 |50.5 |68.7 |8.4 | |35.9 |52.4
[YOLOv5l6][assets] |1280 |53.4 |53.4 |71.1 |12.3 | |77.2 |117.7
[YOLOv5x6][assets] |1280 |**54.4** |**54.4** |**72.0** |22.4 | |141.8 |222.9
| | | | | | || |
[YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8 | |- |-
<details>
<summary>Table Notes (click to expand)</summary>
* AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy.
* AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
* Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`
* All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
* Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale augmentation. **Reproduce TTA** by `python test.py --data coco.yaml --img 1536 --iou 0.7 --augment`
</details>
## Requirements
Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run:
<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
```bash
$ pip install -r requirements.txt
```
## Tutorials
* [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
* [Supervisely Ecosystem](https://github.com/ultralytics/yolov5/issues/2518) ð NEW
* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) â NEW
* [TorchScript, ONNX, CoreML 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
* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)
## Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
- **Google Colab and Kaggle** notebooks with free GPU: <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>
- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <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>
## Inference
`detect.py` runs inference on a variety of sources, downloading 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
file.jpg # image
file.mp4 # video
path/ # directory
path/*.jpg # glob
'https://youtu.be/NUsoVlDFqZg' # YouTube video
'rtsp://example.com/media.mp4' #
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
YOLO项目,工具源码,适合毕业设计、课程设计作业,所有源码均经过严格测试,可以直接运行,可以放心下载使用。有任何使用问题欢迎随时与博主沟通,第一时间进行解答! 软件开发设计:PHP、QT、应用软件开发、系统软件开发、移动应用开发、网站开发C++、Java、python、web、C#等语言的项目开发与学习资料 硬件与设备:单片机、EDA、proteus、RTOS、包括计算机硬件、服务器、网络设备、存储设备、移动设备等 操作系统:LInux、IOS、树莓派、安卓开发、微机操作系统、网络操作系统、分布式操作系统等。此外,还有嵌入式操作系统、智能操作系统等。 云计算与大数据:数据集、包括云计算平台、大数据分析、人工智能、机器学习等,云计算是一种基于互联网的计算方式,通过这种方式,共享的软硬件资源和信息可以按需提供给计算机和其他设备。
资源推荐
资源详情
资源评论
收起资源包目录
毕业设计&课程设计-Use yolov5 for traffic sign detection.zip (124个子文件)
Dockerfile 821B
bus.jpg 476KB
test2.jpg 274KB
test1.jpg 262KB
test3.jpg 219KB
test4.jpg 201KB
zidane.jpg 165KB
LICENSE 11KB
README.md 11KB
README.md 2KB
README.md 2KB
bug-report.md 2KB
feature-request.md 737B
question.md 140B
README.md 16B
datasets.py 44KB
train.py 33KB
general.py 28KB
plots.py 18KB
test.py 17KB
common.py 16KB
wandb_utils.py 16KB
yolo.py 13KB
torch_utils.py 12KB
loss.py 9KB
detect.py 9KB
metrics.py 9KB
autoanchor.py 7KB
export.py 7KB
hubconf.py 6KB
experimental.py 5KB
google_utils.py 5KB
activations.py 4KB
voc_label.py 2KB
resume.py 1KB
restapi.py 1KB
make_text.py 939B
log_dataset.py 800B
example_request.py 299B
__init__.py 0B
__init__.py 0B
__init__.py 0B
__init__.py 0B
__init__.py 0B
datasets.cpython-36.pyc 33KB
datasets.cpython-37.pyc 33KB
general.cpython-36.pyc 23KB
general.cpython-37.pyc 23KB
common.cpython-36.pyc 19KB
common.cpython-37.pyc 19KB
plots.cpython-36.pyc 16KB
plots.cpython-37.pyc 16KB
torch_utils.cpython-36.pyc 11KB
torch_utils.cpython-37.pyc 11KB
wandb_utils.cpython-36.pyc 11KB
wandb_utils.cpython-37.pyc 11KB
test.cpython-36.pyc 11KB
test.cpython-37.pyc 11KB
yolo.cpython-36.pyc 11KB
yolo.cpython-37.pyc 11KB
metrics.cpython-36.pyc 7KB
metrics.cpython-37.pyc 7KB
loss.cpython-36.pyc 6KB
loss.cpython-37.pyc 6KB
autoanchor.cpython-36.pyc 6KB
autoanchor.cpython-37.pyc 6KB
experimental.cpython-36.pyc 6KB
experimental.cpython-37.pyc 6KB
google_utils.cpython-36.pyc 3KB
google_utils.cpython-37.pyc 3KB
__init__.cpython-37.pyc 154B
__init__.cpython-36.pyc 150B
__init__.cpython-37.pyc 141B
__init__.cpython-37.pyc 140B
__init__.cpython-36.pyc 137B
__init__.cpython-36.pyc 136B
get_voc.sh 4KB
get_argoverse_hd.sh 2KB
userdata.sh 1KB
get_coco.sh 962B
mime.sh 780B
get_coco128.sh 618B
download_weights.sh 277B
train.sh 98B
requirements.txt 675B
additional_requirements.txt 105B
objects365.yaml 7KB
anchors.yaml 3KB
VisDrone.yaml 3KB
SKU-110K.yaml 2KB
yolov5-p7.yaml 2KB
GlobalWheat2020.yaml 2KB
yolov5x6.yaml 2KB
yolov5m6.yaml 2KB
yolov5s6.yaml 2KB
yolov5l6.yaml 2KB
yolov5-p6.yaml 2KB
yolov5-p2.yaml 2KB
coco.yaml 2KB
hyp.scratch.yaml 2KB
共 124 条
- 1
- 2
资源评论
妄北y
- 粉丝: 1w+
- 资源: 1万+
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