<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>
<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://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
</div>
<br>
<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://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>
<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>
<!--
<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>
[**Python>=3.6.0**](https://www.python.org/) is required with all
[requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including
[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/):
<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
```bash
$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt
```
</details>
<details open>
<summary>Inference</summary>
Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36). Models automatically download
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 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
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
```
</details>
<details>
<summary>Training</summary>
Run commands below to reproduce results
on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on
first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the
largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices).
```bash
$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 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
* [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)
</details>
## <div align="center">Environments and Integrations</div>
Get started in seconds with our verified environments and integrations,
including [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme) for automatic YOLOv5 experiment
logging. 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.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="15%"/>
</a>
<a href="https://www.kaggle.com/ultralytics/yolov5">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="15%"/>
</a>
<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="15%"/>
</a>
<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="15%"/>
</a>
<a href="https://github.com/ultralytics/yolov5/wiki/GCP-Qui
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温馨提示
利用yolov5和TensorRT从0到1实现目标检测的模型训练到模型部署全过程 利用TensorRT加速推理速度是以时间换取精度的做法,意味着在推理速度上升的同时将会有精度的下降,不过不用太担心,精度下降微乎其微。此外,要有NVIDIA显卡,经测试,CUDA10.2可以支持20系列显卡及以下,30系列显卡需要CUDA11.x的支持,并且目前有bug。 win10 Visual Studio 2019 NVIDIA GeForce RTX 2060 opencv-3.4.3-vc14_vc15 cuda_10.2.89_441.22_win10 cudnn-10.2-windows10-x64-v7.6.5.32 TensorRT-7.0.0.11.Windows10.x86_64.cuda-10.2.cudnn7.6 cmake-3.21.2-windows-x86_64 、将训练阶段得到的.pt模型转化为.wts中间模型 利用Cmake软件创建VS工程 wts转化为engine
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利用yolov5和TensorRT从0到1实现目标检测的模型训练到模型部署全过程 (118个子文件)
model_process.cpp 18KB
Detector.cpp 6KB
calibrator.cpp 3KB
calibrator.cpp 3KB
yololayer.cu 12KB
yololayer.cu 12KB
Dockerfile 821B
.gitignore 4KB
.gitignore 281B
.gitignore 281B
dirent.h 22KB
dirent.h 22KB
logging.h 16KB
logging.h 16KB
yololayer.h 5KB
yololayer.h 5KB
Detector.h 2KB
utils.h 1KB
utils.h 1KB
calibrator.h 1KB
calibrator.h 1KB
macros.h 462B
macros.h 462B
cuda_utils.h 417B
cuda_utils.h 417B
DetectorWrapper.h 115B
common.hpp 13KB
common.hpp 13KB
bus.jpg 476KB
zidane.jpg 165KB
LICENSE 34KB
README.md 13KB
CONTRIBUTING.md 5KB
README.md 4KB
README.md 2KB
readme.md 334B
datasets.py 41KB
train.py 31KB
general.py 30KB
yolov5_trt.py 18KB
plots.py 18KB
common.py 17KB
common.py 17KB
val.py 16KB
metrics.py 13KB
torch_utils.py 13KB
yolo.py 13KB
detect.py 12KB
augmentations.py 11KB
loss.py 9KB
export.py 8KB
autoanchor.py 7KB
__init__.py 6KB
downloads.py 6KB
callbacks.py 6KB
hubconf.py 6KB
experimental.py 5KB
convert_onnx.py 4KB
activations.py 4KB
yolov5x.py 2KB
yolov5l.py 2KB
best.py 2KB
yolov5m.py 2KB
yolov5s.py 2KB
gen_wts.py 1KB
gen_wts.py 1KB
resume.py 1KB
restapi.py 1KB
pre_export.py 864B
example_request.py 299B
get_anchors.py 269B
learn.py 187B
__init__.py 0B
__init__.py 0B
__init__.py 0B
userdata.sh 1KB
get_coco.sh 936B
mime.sh 780B
get_coco128.sh 658B
download_weights.sh 479B
CMakeLists.txt 2KB
CMakeLists.txt 2KB
requirements.txt 703B
additional_requirements.txt 105B
Objects365.yaml 7KB
xView.yaml 5KB
VOC.yaml 3KB
anchors.yaml 3KB
VisDrone.yaml 3KB
Argoverse.yaml 3KB
SKU-110K.yaml 2KB
coco.yaml 2KB
yolov5-p7.yaml 2KB
GlobalWheat2020.yaml 2KB
yolov5s6.yaml 2KB
yolov5m6.yaml 2KB
yolov5x6.yaml 2KB
coco128.yaml 2KB
yolov5l6.yaml 2KB
hyp.scratch-p6.yaml 2KB
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