<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~
基于YOLOv5的11种动物识别(Kaggle)
共2000个文件
txt:1002个
jpg:907个
yaml:39个
需积分: 5 0 下载量 92 浏览量
2024-04-19
16:11:09
上传
评论
收藏 73.52MB ZIP 举报
温馨提示
该资源用于部署到Kaggle使用GPU进行模型训练。
资源推荐
资源详情
资源评论
收起资源包目录
基于YOLOv5的11种动物识别(Kaggle) (2000个子文件)
.gitattributes 75B
255_jpg.rf.4572f7ca6978b22c9d118dec88b18116.jpg 98KB
81_jpg.rf.64ea6ce7206037e8a6f72f7975240526.jpg 94KB
278_jpg.rf.0b3b0a40b64119dca7242ec728ad9c28.jpg 82KB
240_jpg.rf.16ca73eb6403c8dbce61c8407e0540a7.jpg 81KB
127_jpg.rf.2f482a838ae213f8bb8019150031a2f2.jpg 79KB
80_jpg.rf.b883f4f2814f352d76c1a36a3a7a9cae.jpg 79KB
64_jpg.rf.13cab5175fd5dd2094c245754fcd0c2b.jpg 78KB
118_jpg.rf.7b32ac377ff6af044492937bc1cee616.jpg 78KB
181_jpg.rf.1e777d9454d419b80f5aeca7c2ce1d2d.jpg 77KB
186_jpg.rf.53fdf4acc911edc297480fd8c6e0bfed.jpg 77KB
71_jpg.rf.22967a7d4ace10e20d0a15bb69a23792.jpg 77KB
729_jpg.rf.b59fd356f0d475830aba4b52f6ded812.jpg 76KB
219_jpg.rf.dcb7bfcaa67a0eabc9a2fece881c8139.jpg 74KB
749_jpg.rf.f0148e9c6438ab3bfd6f0d0ad0633135.jpg 74KB
261_jpg.rf.a14109f7cfa8925cca870d8683cfe0a3.jpg 73KB
96_jpg.rf.012e39888e1937be8a13f492f4fdfbe0.jpg 73KB
108_jpg.rf.e4b08de903a4b851ac7a6cdc552b95b1.jpg 72KB
262_jpg.rf.6db8f40ac8e528c5d0a14259754860a1.jpg 72KB
197_jpg.rf.8b988d06a02e91d16b45eb8a3ba1933a.jpg 72KB
177_jpg.rf.c24705d22b496a4e60a9aee2219e4644.jpg 71KB
290_jpg.rf.8e78143cf2ad3e0f3a82c184a017b834.jpg 71KB
88_jpg.rf.a886a15462057ce5a1e28f8959b2376f.jpg 71KB
207_jpg.rf.0607e7930a6e2dafec57cbc8a7264c93.jpg 71KB
998_jpg.rf.a5b998a8d7fe69261274b5b4894beb1b.jpg 70KB
334_jpg.rf.11bed90d80ce1178ae1ed06b4178f448.jpg 70KB
20_jpg.rf.a11846ee1e786d2c4e23f8d1843ad9cd.jpg 70KB
157_jpg.rf.bc6efc5948958fd3049fb64269996135.jpg 70KB
385_jpg.rf.d955af99f5e32002361c4da25edce426.jpg 70KB
389_jpg.rf.45f6baab1d345465c9ad928b07b212ad.jpg 70KB
288_jpg.rf.4f2f43ec66ccef089cae7a3ecc067c4d.jpg 69KB
271_jpg.rf.45bca9361b179a1f2727b2d184f1e0f3.jpg 69KB
84_jpg.rf.cd5d3c8e668c6fa8ce581c3b349af411.jpg 69KB
169_jpg.rf.70708d7d114c94421f1c70d7cf885344.jpg 69KB
74_jpg.rf.d1e1d5dfbfd2e8265b610bd6e4bf87dc.jpg 69KB
147_jpg.rf.c87970731c38f1858be3eaa38757ed42.jpg 68KB
297_jpg.rf.7ff7edc378738bf92c7d2071062778d0.jpg 68KB
816_jpg.rf.781a47d2040fded0ec951f3832dfe46a.jpg 68KB
190_jpg.rf.271eee298cb05add64bf1576c9a13e6e.jpg 68KB
721_jpg.rf.c684d845c0bbe3faca9eefc51dc5410c.jpg 68KB
775_jpg.rf.81ffded42680291421b6753c807e4044.jpg 67KB
116_jpg.rf.b6c56c732160358f144ec049e6128e4d.jpg 67KB
891_jpg.rf.4a47aa18b39d61c2881e8868475c56d7.jpg 67KB
79_jpg.rf.2f9eeeb92d39f58b884003bf7ce7eaea.jpg 67KB
42_jpg.rf.bbd861a1a1977161cfe325f520551a9d.jpg 66KB
200_jpg.rf.90c5432cf7c6c1d9fc6bf1d5cedffe15.jpg 66KB
991_jpg.rf.858705ec32ea72443f20bdff456d3ac6.jpg 66KB
142_jpg.rf.a5c46cd2bcf23276b0e3b986818d1a7b.jpg 66KB
553_jpg.rf.1ee5701c8227b5e1afbc0ae1bf65804c.jpg 66KB
543_jpg.rf.171dbf110fec4f9066dcdcfb74b3afda.jpg 66KB
234_jpg.rf.dd6d17467aea8c95c434808d7d686a99.jpg 66KB
191_jpg.rf.793f0786613151bf4136c1d108885202.jpg 65KB
131_jpg.rf.c9b8e45d50ab4b33d21eaff94cfd042c.jpg 65KB
759_jpg.rf.72abc3fa764e92c95bee1b6360bcd8c5.jpg 65KB
750_jpg.rf.7d727815880d6f73c1b1e82be3edfc11.jpg 65KB
41_jpg.rf.ea8e6427de21284ffb0bbd0daa67b999.jpg 65KB
10_jpg.rf.0fa9f328d57f389967a2715109f762b6.jpg 65KB
526_jpg.rf.72c1ad2ad3ae2c8cf0deb2d2a3189f5b.jpg 65KB
257_jpg.rf.59689c390804004403ced29a38310a54.jpg 65KB
722_jpg.rf.d302da5aa628c98b8917e91a9084c3f1.jpg 65KB
337_jpg.rf.ad3dfaacd1820f75a2631220d7f414d1.jpg 65KB
126_jpg.rf.5f2c2a1836e4ac39b2f5a66e6187e38e.jpg 65KB
221_jpg.rf.35c7d12a590d4f63bfa90f5f7c572853.jpg 64KB
373_jpg.rf.2249917ffbadb7520fd446666b1c3c16.jpg 64KB
364_jpg.rf.f048320efd67704524525a148d8b596f.jpg 64KB
785_jpg.rf.0903220c7e119ea2f408543c9ba35124.jpg 64KB
372_jpg.rf.aa9416f357da0d037ddad2678de4e820.jpg 64KB
927_jpg.rf.26349b273cbaf40c37fde53a2f8d5eab.jpg 64KB
21_jpg.rf.b50d0a766f5f50017684848303e6da81.jpg 64KB
888_jpg.rf.495e0573b12b31b0453af0ed4cef4a1d.jpg 64KB
541_jpg.rf.33a03e73f2b9897c823b2ebd3e648edb.jpg 64KB
155_jpg.rf.1ca4fe3ab28e4d919e700534d3598445.jpg 64KB
807_jpg.rf.dd4a154281d18d1037a00e05d1cd737f.jpg 64KB
861_jpg.rf.5224c50ddbbda0f2ecaff55dfb19256f.jpg 64KB
447_jpg.rf.2b02e2f474c7960ec15522ba9f177133.jpg 64KB
182_jpg.rf.4f9788479c03f68a1e6195f60c2bb4d8.jpg 64KB
748_jpg.rf.d48573b082fa343c7192cbb2f8062778.jpg 64KB
395_jpg.rf.13d8b89661ed12275b58b323169dd8f1.jpg 64KB
148_jpg.rf.c47f66c246f052e85e6d769e96162696.jpg 64KB
144_jpg.rf.3f247e177a0b205f8edd72914ecaa407.jpg 63KB
160_jpg.rf.5092b6872f52d97efb84f9a5f57e0c6e.jpg 63KB
542_jpg.rf.558376b070c3e7385b52510dc0aef1a9.jpg 63KB
273_jpg.rf.63c670bd0e0eed110d4fe8e86e44bdfc.jpg 63KB
188_jpg.rf.9495295247d400ed4fa3194ad98afa99.jpg 63KB
263_jpg.rf.7e4fa44d150371016058e6439aa8f1c1.jpg 63KB
8_jpg.rf.d598f44ad1af1bf18bd38e32bbea99d4.jpg 63KB
357_jpg.rf.e898326fad5d3e5f1c4f9dfd1c331bf4.jpg 63KB
138_jpg.rf.050dd49c861a68396e995c097b0116c9.jpg 63KB
960_jpg.rf.f4159cc06e770bdf85afe27b6d9fe576.jpg 63KB
199_jpg.rf.414fb8209e8d77b09df894165f35f3d6.jpg 63KB
533_jpg.rf.06fb9c90b8578ee6e8bdb28b6aa52bb5.jpg 62KB
617_jpg.rf.f846385c4b46817e0bd73b24a08d98f4.jpg 62KB
532_jpg.rf.e79c3413ba00245133a6a0b62f71a068.jpg 62KB
224_jpg.rf.2457971aa435262c3610270d703191ba.jpg 62KB
896_jpg.rf.8d4b149f00ad8cacc0de5cb06f8c37d1.jpg 62KB
145_jpg.rf.bd94e735db75df22945e59fd1bfa91bd.jpg 62KB
140_jpg.rf.8f137d8a8839ecaf412ac62275fc4a59.jpg 62KB
251_jpg.rf.4bd330fd597eb1a0628b219f8329fb34.jpg 62KB
754_jpg.rf.483b3923b80716eb388fb54c0f569cbb.jpg 62KB
701_jpg.rf.64117f82197d0350f60813eae3431d21.jpg 62KB
共 2000 条
- 1
- 2
- 3
- 4
- 5
- 6
- 20
资源评论
这个函数可导
- 粉丝: 1494
- 资源: 3
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
- hdmi in视频采集,输出到hdmi out, 支持HDMI指令控制,支持TFTP远程下载图片
- 批量word文件内容替换工具1.0 (批量实现多个 Word 文档文件文字替换利器).exe
- Cartoon GUI Pack 1.2.zip
- 【数据集和代码】基于加速度传感器的步态识别行人分类实验(可做步态识别)
- 我分享个魔兽内存修改器
- Python毕业设计基于Django的网易云数据分析可视化大屏系统的设计与实现+使用说明+全部资料(优秀项目).zip
- mp3 idv2,idv1,frame分析工具
- 鹈鹕优化算法POA MATLAB源码, 应用案例为函数极值求解以及优化svm进行分类,代码注释详细,可结合自身需求进行应用
- Python毕业设计基于Django的网易云数据分析可视化大屏系统的设计与实现+使用说明+全部资料(高分项目).zip
- 蛇优化算法SO MATLAB源码, 应用案例为函数极值求解以及优化svm进行分类,代码注释详细,可结合自身需求进行应用
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