<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>
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<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
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2021第三届华为云人工智能大赛 · 无人车挑战杯 方案分享.zip (249个子文件)
insert_op_conf.cfg 264B
torchscript_mask_rcnn.cpp 6KB
Dockerfile 821B
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ZIP.iml 606B
HWCloud2021.iml 323B
index 161B
tutorial.ipynb 55KB
1004.jpg 274KB
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1.jpg 216KB
frost5.jpg 152KB
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centernet2_teaser.jpg 41KB
frost4.jpg 36KB
launch.json 319B
skill_info.json 143B
result.json 36B
skill_config.json 2B
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README.md 11KB
MODEL_ZOO.md 7KB
CONTRIBUTING.md 5KB
readme.md 5KB
README.md 5KB
README.md 3KB
README.md 3KB
readme.md 3KB
readme.md 3KB
readme.md 2KB
README.md 2KB
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bifpn_fcos.py 17KB
dlafpn.py 17KB
dla.py 17KB
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objects365.py 13KB
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plain_train_net.py 8KB
__init__.py 8KB
custom_roi_heads.py 8KB
train_net.py 8KB
cspdarknet53.py 8KB
demo.py 7KB
ensemble_boxes_wbf.py 7KB
autoanchor.py 7KB
lr_scheduler.py 7KB
util.py 7KB
custom_fast_rcnn.py 6KB
hubconf.py 6KB
benchmark.py 6KB
downloads.py 6KB
train_net.py 6KB
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- weixin_581631032023-10-18资源和描述一致,质量不错,解决了我的问题,感谢资源主。
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