<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>
<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
* [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
* [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</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.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:/
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
yolo5中大小各种尺度红外无人机检测,训练好的权重,可以直接使用,并附有8000左右yolo小型型固定翼无人机检测数据集,数据集目录已经配置好,yolo格式(txt)的标签,划分好 train,val, test,并附有data.yaml文件,yolov5、yolov7、yolov8等算法可以直接进行训练模型, 数据集和检测结果参考: https://blog.csdn.net/zhiqingAI/article/details/124230743 https://blog.csdn.net/zhiqingAI/article/details/136952543 数据集配置目录结构data.yaml: nc: 1 names: ['IR_Drone']
资源推荐
资源详情
资源评论
收起资源包目录
YOLOv5算法中大小各种尺度红外无人机检测权重+8000红外无人机数据集+使用教程 (2000个子文件)
README.md 14KB
README.md 10KB
CONTRIBUTING.md 5KB
【yolov3-YOLOv5-yolov7-yolov8环境配置-教程2】.md 5KB
README.md 2KB
bug-report.md 1KB
feature-request.md 739B
question.md 139B
【yolov3-YOLOv5-yolov7-yolov8环境配置-教程1】.pdf 6.55MB
【yolov3-YOLOv5-yolov7-yolov8环境配置-教程2】.pdf 580KB
datasets.py 43KB
general.py 33KB
wandb_utils.py 25KB
plots.py 19KB
export.py 16KB
torch_utils.py 14KB
metrics.py 13KB
augmentations.py 11KB
loss.py 9KB
autoanchor.py 7KB
__init__.py 6KB
hubconf.py 6KB
downloads.py 6KB
activations.py 4KB
callbacks.py 2KB
resume.py 1KB
restapi.py 1KB
sweep.py 989B
log_dataset.py 891B
example_request.py 299B
__init__.py 0B
__init__.py 0B
__init__.py 0B
userdata.sh 1KB
get_coco.sh 900B
mime.sh 780B
get_coco128.sh 615B
download_weights.sh 443B
pic_323_jpgrfa9ae7f57dbe50eb66c5b390f21225c76_jpg.rf.ba68bd82f40ca8905a815dd3bc39187d.txt 395B
234_jpgrfa37cdd2a7050c2786f17385f55cb7a62_jpg.rf.6d978e9bd7f2aeb47ea3f6baa1a09127.txt 362B
5_pngrf4b4e61b4669b2015b92dcf596ef60fbe_jpg.rf.8ee362e167ca4791b9be9225f58417c8.txt 329B
0001_jpgrfaf820ca2a92fb2c4372b6cdc9254d056_jpg.rf.8a2db82c8e21c8faba2af8d4a920912c.txt 273B
8_jpgrf11218a2a74b3e34f85c8f7bd32be5ba8_jpg.rf.09e5e08470a23e8bbce54ede476dbaf7.txt 267B
782_pngrf20abc61101c9d8ef64adf79196c06c77_jpg.rf.c867402ec7b3318a92d7c20999d1a61e.txt 184B
782_pngrf20abc61101c9d8ef64adf79196c06c77_jpg.rf.1c4dce61667785041101af21903701ca.txt 162B
pic_935_jpgrf39b28c98a2178eab406398f0c7fb8837_jpg.rf.7fb6e7cd6cc4ab15aebff241d4cc231b.txt 135B
16_pngrf09571ce57bdcf2e63a4899a6eac241ca_jpg.rf.9613a05a6ee3025fb2f8fa27a6648cf9.txt 132B
pic_218_jpgrf039d39aa161eec99e6e61de019b12e3e_jpg.rf.c69255452b50bea8c7755739f9d716c9.txt 116B
0090_jpgrf19b0a6272ee70076b6ceac224f2625f1_jpg.rf.f3612be3ac07045088cb91afcabbbc65.txt 109B
additional_requirements.txt 105B
pic_789_jpgrfd8292e17779976da59007127ad0e127b_jpg.rf.92a2a7362928811faafa8cdc51ff7839.txt 100B
0312_jpgrff757ad14a0d461b26a80e7f674c1ca7d_jpg.rf.e0458577274fbb096b48747aca44430e.txt 96B
0090_jpgrf19b0a6272ee70076b6ceac224f2625f1_jpg.rf.51ab14ea96f8a0998890a0388612c0b2.txt 92B
pic_116_jpgrf3f3542757f7e43ef1cf454afe3da5b9e_jpg.rf.255e896d69f029bd64187b30ba0904fd.txt 81B
784_pngrfa814b14f86e32cc7b1242e7a446f7243_jpg.rf.2f3748786b53af87dd7bb136a77f8f45.txt 81B
192_jpgrf35519a51c719d8d80fd0e5fefcd8d2a1_jpg.rf.792ec2e6dfa0382ba78d9f029e4334ee.txt 81B
41_pngrf2cc2a916171b6f854f10a0641d98bad6_jpg.rf.965777d0f332a8df6d40e2d1c5429298.txt 81B
339_jpgrf791202cfe9e2994331da2ea40c6a793c_jpg.rf.6fdb6a18c6fa9b80d9df94f6d7af0317.txt 80B
302_jpgrfd392c02dfcb97f0d258a7d7fb30e3c7a_jpg.rf.e869ea9c50818268d4bd87a604aa7e81.txt 80B
278_jpgrfa79107bce086c2ddc67ddd1498a407f6_jpg.rf.90a69fe28b6e94e0d6aea0278fc7ed54.txt 80B
281_jpgrfd17cb356d11f0b5409d40a37b632400b_jpg.rf.aced063de4037972656e60601261bc80.txt 80B
247_jpgrf6b6b804dbf5c1090aa976b0c8c178047_jpg.rf.4f18a9d82e9af73c44421aedbb88c81a.txt 80B
304_jpgrf639f29738ac7a3fb0a08bf8f0d5e7191_jpg.rf.d92f7f4b7b56946d730deeee5939fe19.txt 79B
72_jpgrf002fcffa86749a1f4139e44c6d065cc7_jpg.rf.4e8b65d629ff8ca06885123446ddc3ed.txt 79B
72_jpgrf002fcffa86749a1f4139e44c6d065cc7_jpg.rf.58e3d7beebad3ed0aa5e05c1354a0626.txt 79B
247_jpgrf6b6b804dbf5c1090aa976b0c8c178047_jpg.rf.b268a7470d803b1cbc6964b3eff0ec47.txt 78B
0225_jpgrf8f0b4b92d9bdf7b4bec9a3895f8616cb_jpg.rf.eb3c9ef5c282f4c01defc33bd47182c2.txt 78B
0324_jpgrff82a947a0618c16ae1f5647305065e06_jpg.rf.c958df5000c5534f9bf38edc6bd802e7.txt 78B
24_jpgrf764e5b59901f16d6812d0091ea81b2b4_jpg.rf.dfb2f3904eb131ec4d7548b52abffd38.txt 78B
427_jpgrf3187ebbb6c5ffb8338ea08640f82d4fb_jpg.rf.47e0c0fbbcb6ed8d075cd1b8e33ea3f4.txt 78B
0239_jpgrfa176730502a63577488cd9820a494710_jpg.rf.3c1d5c399b00f1de834de7c9d3596ff8.txt 78B
pic_609_jpgrfb31d1e7fa5253d6b44aba564bbcd0a8f_jpg.rf.d5a86a04645e056ddbebc1c045b08a5f.txt 78B
0287_jpgrfbfdc382213b2778ec1bf2d2060d6595f_jpg.rf.1ad2602f876314831061876f263c7ac7.txt 78B
pic_730_jpgrf7d6e43606e3815cbb37680f02f6895e9_jpg.rf.b5121e24563504714832419e1d4be9b4.txt 78B
24_jpgrf764e5b59901f16d6812d0091ea81b2b4_jpg.rf.0bc3a46f5d97a243d877a2c4366798dc.txt 78B
252_jpgrf494a40354228dcf2a81b831ab7df8fdd_jpg.rf.6bc520bcca86278511ce8bd4b3814cf1.txt 78B
pic_569_jpgrf727b870bfd3e3cf653b2efc419e8a0ba_jpg.rf.4a15a2df66101eb74adff1ba57c11cac.txt 78B
64_jpgrf7383eb72801c40d53a5738826b92583f_jpg.rf.638522ddbd3ee8efc4bd23d28bd42e6f.txt 78B
43_jpgrf6f4caa902ae91edccf55d3f7e18612dd_jpg.rf.ddcb8555f1e2189cedfda98247ff3906.txt 78B
pic_569_jpgrf727b870bfd3e3cf653b2efc419e8a0ba_jpg.rf.46c4f6ff4ce4df169661c1c9f08c4055.txt 78B
pic_169_jpgrfa050f09fbeb6600df77b7fad7ce40ec5_jpg.rf.c63af8c6780afb767809b0785a2d6e1a.txt 78B
pic_229_jpgrf4b155a4232316bca934557e95410889c_jpg.rf.8dee9c9a16a12e308b6c6f9074079002.txt 78B
32_jpgrfafec614fa504566387fd190ac1d4d2f4_jpg.rf.39ebeded361829c26f9b4123d559cafc.txt 77B
716_pngrfa2a90c21b5d2bcf81cff5f5714896714_jpg.rf.9e48c5b05375235eb18eba7c184093db.txt 77B
pic_515_jpgrf9b1de08306107f8447192090a810a4e3_jpg.rf.c8b6608b23f1e367113556d62430dc3f.txt 77B
447_jpgrf862e5d98cfbe7503f429ef2553618886_jpg.rf.b606847b84a261fb012c7e0660c03ee8.txt 77B
pic_285_jpgrf8cc2cf1d9d44c36a064fd9edc44f3a9c_jpg.rf.ee462ca3821ad6ddb388e4b9d9de088e.txt 77B
716_pngrfa2a90c21b5d2bcf81cff5f5714896714_jpg.rf.784f00a433110babd7ed2b3a696b89d3.txt 77B
51_jpgrfbae52778a149277ceae197785d77b243_jpg.rf.c8f7ada11b571cd6fe71c61ffa8d90ec.txt 77B
795_pngrf8a01c5014bff97644aefe7e5dd0f9b4c_jpg.rf.4805bd44c006a6f1976cf0a173adf9b1.txt 77B
pic_770_jpgrf74a533591b36229e1365da7ba8884b41_jpg.rf.12d219f97a9977750fd031a8c6031d28.txt 77B
302_jpgrfd392c02dfcb97f0d258a7d7fb30e3c7a_jpg.rf.39bc473e4445c79a43247e94132c1d73.txt 77B
43_jpgrf6f4caa902ae91edccf55d3f7e18612dd_jpg.rf.0cafa025927e3020d274cb9668dfc59b.txt 77B
369_jpgrf5670c836196b983d922e0b7fa70e3a4e_jpg.rf.4b8d9019ff468e8cabc06b35db30602d.txt 77B
0030_jpgrf47f91190d2755f78884328b7bc15c88d_jpg.rf.16361138ac94655eee0d5df6af584ff4.txt 77B
262_jpgrf99e30a457236488894b05d7364fbb4c3_jpg.rf.2a6eaad2fb1fcd05929cea833ffaa90e.txt 77B
806_pngrfec4c2171cc72dc99e93b845b3772500d_jpg.rf.63f5c69716f57250c5f483d9cad8f113.txt 77B
0072_jpgrf7c2116d751a249ae05e0efca453133d7_jpg.rf.e39a4a6e2901021868ade5231650c929.txt 77B
261_jpgrf1a774b86a50733169418cb19494908a6_jpg.rf.04ac75344f177acbe71d21323b0ef57e.txt 77B
261_jpgrf1a774b86a50733169418cb19494908a6_jpg.rf.f85cd176b6ee36c93c62846791c2147c.txt 76B
共 2000 条
- 1
- 2
- 3
- 4
- 5
- 6
- 20
资源评论
stsdddd
- 粉丝: 2w+
- 资源: 729
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
最新资源
- Solo 是一款小而美的开源博客系统,专为程序员设计
- 整站程序天地网络-网络学院全站-tiandinetxy
- 整站程序数字中国全站系统 v1.3.1-dbchinav131
- Java 培训和安置管理系统项目源码(可做毕设参考)
- 宏华水利小程序-网页设计-微信小程序
- 用云电商 uniCloud 版,完整商用级项目,一套 js 解决前端、后端、数据库的全栈开发 serverless 模式永久开源
- 整站程序三雷11种语言建站系统(网络版) v6.0-30tnetwork
- SpringBoot集成mqtt上下线提醒功能设计
- 整站程序情感家园站 v3.0 For 个人版-qgweb30fp.zip
- Java 测验管理系统项目源代码
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
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