# Official YOLOv7
Implementation of paper - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/yolov7-trainable-bag-of-freebies-sets-new/real-time-object-detection-on-coco)](https://paperswithcode.com/sota/real-time-object-detection-on-coco?p=yolov7-trainable-bag-of-freebies-sets-new)
[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/yolov7)
<a href="https://colab.research.google.com/gist/AlexeyAB/b769f5795e65fdab80086f6cb7940dae/yolov7detection.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
[![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2207.02696-B31B1B.svg)](https://arxiv.org/abs/2207.02696)
<div align="center">
<a href="./">
<img src="./figure/performance.png" width="79%"/>
</a>
</div>
## Web Demo
- Integrated into [Huggingface Spaces ����](https://huggingface.co/spaces/akhaliq/yolov7) using Gradio. Try out the Web Demo [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/yolov7)
## Performance
MS COCO
| Model | Test Size | AP<sup>test</sup> | AP<sub>50</sub><sup>test</sup> | AP<sub>75</sub><sup>test</sup> | batch 1 fps | batch 32 average time |
| :-- | :-: | :-: | :-: | :-: | :-: | :-: |
| [**YOLOv7**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) | 640 | **51.4%** | **69.7%** | **55.9%** | 161 *fps* | 2.8 *ms* |
| [**YOLOv7-X**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) | 640 | **53.1%** | **71.2%** | **57.8%** | 114 *fps* | 4.3 *ms* |
| | | | | | | |
| [**YOLOv7-W6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) | 1280 | **54.9%** | **72.6%** | **60.1%** | 84 *fps* | 7.6 *ms* |
| [**YOLOv7-E6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) | 1280 | **56.0%** | **73.5%** | **61.2%** | 56 *fps* | 12.3 *ms* |
| [**YOLOv7-D6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) | 1280 | **56.6%** | **74.0%** | **61.8%** | 44 *fps* | 15.0 *ms* |
| [**YOLOv7-E6E**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt) | 1280 | **56.8%** | **74.4%** | **62.1%** | 36 *fps* | 18.7 *ms* |
## Installation
Docker environment (recommended)
<details><summary> <b>Expand</b> </summary>
``` shell
# create the docker container, you can change the share memory size if you have more.
nvidia-docker run --name yolov7 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolov7 --shm-size=64g nvcr.io/nvidia/pytorch:21.08-py3
# apt install required packages
apt update
apt install -y zip htop screen libgl1-mesa-glx
# pip install required packages
pip install seaborn thop
# go to code folder
cd /yolov7
```
</details>
## Testing
[`yolov7.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) [`yolov7x.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) [`yolov7-w6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) [`yolov7-e6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) [`yolov7-d6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) [`yolov7-e6e.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt)
``` shell
python test.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.65 --device 0 --weights yolov7.pt --name yolov7_640_val
```
You will get the results:
```
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.51206
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.69730
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.55521
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35247
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.55937
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66693
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.38453
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.63765
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.68772
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.53766
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.73549
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83868
```
To measure accuracy, download [COCO-annotations for Pycocotools](http://images.cocodataset.org/annotations/annotations_trainval2017.zip) to the `./coco/annotations/instances_val2017.json`
## Training
Data preparation
``` shell
bash scripts/get_coco.sh
```
* Download MS COCO dataset images ([train](http://images.cocodataset.org/zips/train2017.zip), [val](http://images.cocodataset.org/zips/val2017.zip), [test](http://images.cocodataset.org/zips/test2017.zip)) and [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip). If you have previously used a different version of YOLO, we strongly recommend that you delete `train2017.cache` and `val2017.cache` files, and redownload [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip)
Single GPU training
``` shell
# train p5 models
python train.py --workers 8 --device 0 --batch-size 32 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml
# train p6 models
python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights '' --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml
```
Multiple GPU training
``` shell
# train p5 models
python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch-size 128 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml
# train p6 models
python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_aux.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch-size 128 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights '' --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml
```
## Transfer learning
[`yolov7_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7_training.pt) [`yolov7x_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x_training.pt) [`yolov7-w6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6_training.pt) [`yolov7-e6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6_training.pt) [`yolov7-d6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6_training.pt) [`yolov7-e6e_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e_training.pt)
Single GPU finetuning for custom dataset
``` shell
# finetune p5 models
python train.py --workers 8 --device 0 --batch-size 32 --data data/custom.yaml --img 640 640 --cfg cfg/training/yolov7-custom.yaml --weights 'yolov7_training.pt' --name yolov7-custom --hyp data/hyp.scratch.custom.yaml
# finetune p6 models
python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/custom.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6-custom.yaml --weights 'yolov7-w6_training.pt' --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml
```
## Re-parameterization
See [reparameterization.ipynb](tools/reparameterization.ipynb)
## Inference
On video:
``` shell
python detect.py --weights
没有合适的资源?快使用搜索试试~ 我知道了~
YOLOv7算法DMS驾驶员抽烟-打电话-喝水-吃东西分神检测+数据集
共2000个文件
txt:1994个
pdf:3个
md:3个
1.该资源内容由用户上传,如若侵权请联系客服进行举报
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
版权申诉
0 下载量 115 浏览量
2024-04-26
22:24:10
上传
评论
收藏 740.11MB ZIP 举报
温馨提示
yolov7算法DMS驾驶员抽烟-打电话-喝水-吃东西检测, 包含5000多张DMS驾驶员抽烟-打电话-喝水-吃东西检测数据集,数据集目录已经配置好,划分好 train,val, test,并附有data.yaml文件,yolov5、yolov7、yolov8,yolov9等算法可以直接进行训练模型,txt格式标签, 数据集和检测结果参考:https://blog.csdn.net/zhiqingAI/article/details/124230743 数据集配置目录结构data.yaml: train: ../train/images val: ../valid/images test: ../test/images nc: 4 names: ['drinking', 'eating', 'mobile use', 'smoking']
资源推荐
资源详情
资源评论
收起资源包目录
YOLOv7算法DMS驾驶员抽烟-打电话-喝水-吃东西分神检测+数据集 (2000个子文件)
LICENSE.md 34KB
README.md 14KB
README.md 7KB
【yolov3-YOLOv5-yolov7-yolov8环境配置-教程1】.pdf 6.55MB
yolov7.pdf 5.85MB
【yolov3-YOLOv5-yolov7-yolov8环境配置-教程2】.pdf 580KB
image21_png.rf.88d1a1230c5b906e02faa20fb0747e93.txt 229B
image21_png.rf.1122945cf71fe20673fba7acffe26ec4.txt 228B
使用说明.txt 185B
image2431_png.rf.a557b47dec9ac4249afe806fe16829f9.txt 81B
image2434_png.rf.42b323c586dcfbbc82efc3099dde7cb0.txt 81B
image2431_png.rf.fd55d991baa13d9cc33b29738e75c380.txt 81B
image2434_png.rf.e3a8fcc195cd7a1d52c0af930e206c72.txt 81B
image2443_png.rf.9e427296e66baff440259b5dc021c482.txt 76B
image2433_png.rf.f2258171c4342e0a41d36fe7ca429c22.txt 76B
image2432_png.rf.c24e046a205e9425031a4f6287df224d.txt 76B
image2433_png.rf.55737d6b0d354e91b8891c348c261566.txt 76B
image2445_png.rf.2ac6ea3ae90903c0cb4a48a9e1de3f0e.txt 74B
image2446_png.rf.7cddac85a42039d2b1e213ebc440ce6e.txt 73B
image2446_png.rf.1acfcac890435fe0729d84bb4c993a3a.txt 73B
image704_png.rf.b7dc21e5de9d806c93e3e9fd27080b6e.txt 72B
image2440_png.rf.da2b9c0414a444de753a3d3da2805837.txt 71B
image1307_png.rf.5f4de13326413518568515bdc4c7d081.txt 43B
image1731_png.rf.81ed2801a54bdaaa1f6c717ef11400bd.txt 43B
image1307_png.rf.a8c502bf3cf4cade02f85402113b8c71.txt 43B
image863_png.rf.f7b3902e758e48d7c19c35f8acf74dae.txt 43B
image1534_png.rf.468eda74a7553602c1a0792fdc5f0bd2.txt 43B
image1894_png.rf.ad5f715c0758547e4edfc492eb4874b3.txt 43B
image1533_png.rf.e05b51653f51733951645906e427a30a.txt 43B
image1531_png.rf.da527e7792de01798c95d06622b09935.txt 43B
image1532_png.rf.af6cdd6dd4cdd5495fe478e5446507bb.txt 43B
image1731_png.rf.1407f6e1c93cfd3739458c705816c6db.txt 43B
image915_png.rf.b8383030d7d28b6766f8e9cca13bee9e.txt 43B
image2260_png.rf.75a260181a73754e9baaba9877e5fded.txt 43B
image2132_png.rf.f7d233c7d68430c967065701460ebca8.txt 43B
image915_png.rf.5b9bdeb0e2efbd1b01b867a6520a5a61.txt 43B
image1894_png.rf.992a94618082a220d9c46ce12d674d75.txt 43B
image1258_png.rf.ed3490082cd7e9ef5417b3e4d31a1510.txt 43B
image1505_png.rf.f2870f66cfe5205b5c301a1c6aeb9ce6.txt 43B
image2158_png.rf.69f373f6aa6077d724b37b440f200e6a.txt 43B
image1995_png.rf.532d0d6fe2fd651e49abcbea5f867ec0.txt 43B
image2962_png.rf.8b1ffce4b8059bcc3a67f91ad84565a9.txt 43B
image2193_png.rf.3a27650c8f0f3647fddebf0a7dfe0e9d.txt 43B
image428_png.rf.6713954dd375c1dfeab12a972c80783b.txt 43B
image843_png.rf.3bd164d3773d76f1b9bb9334e44046b0.txt 43B
image644_png.rf.4147fc6982b805ab2f9e1647410f348d.txt 43B
image1417_png.rf.3b424dc36d4330f758c96bfd548f3857.txt 43B
image1729_png.rf.5608f623cf60ebc4061f33938ba12378.txt 43B
image1307_png.rf.32d801d65708dde3d014f9c628489c4d.txt 43B
image1258_png.rf.8c481e4b74af23d3ae6e3fb1bf0174a7.txt 43B
image2145_png.rf.b5c3ca9369f1d246b09b087fa1c50eae.txt 43B
image2888_png.rf.8018d03052b28a1233a95338519a937d.txt 43B
image2962_png.rf.e39e94d5a74f76288bf78f59a39cf961.txt 43B
image2888_png.rf.623071c1672bcfcc431f6790b87fcbd4.txt 43B
image2159_png.rf.eb02d42f1d6ed215e7ed92dc389db38d.txt 43B
image1731_png.rf.b09258df05c26e01f8b30474735faad8.txt 43B
image1258_png.rf.9a776b619c2bb9f881184789e2e5167b.txt 43B
image842_png.rf.42b4e42b43db96740e56bfde29299a58.txt 43B
image843_png.rf.985f78b02d238ce66c8dd69f8028c0ba.txt 43B
image2145_png.rf.7e560dfa973d4134b5f62542f42cbf1f.txt 43B
image2132_png.rf.b44ba767c12f27b74fc8dee3cc2fc8b7.txt 43B
image1534_png.rf.593a059442b9ebeac13b41730dc08af4.txt 43B
image1995_png.rf.2fc7b8104791abedf7ff4d72b1f5c583.txt 43B
image2140_png.rf.c6c15d1df4b58b28593917038158e248.txt 42B
image2421_png.rf.d8195df34266642bb7345777bd9d6e4f.txt 42B
image89_png.rf.91bda0189d985861c53725db6e59d0c2.txt 42B
image814_png.rf.b4be1a6ec44040cee2aa2ec3d6ddf0ff.txt 42B
image1990_png.rf.d15535a48ea6526992e777cda65313dc.txt 42B
image2327_png.rf.7b6961e3c6cf30fa26ef6fa37091e771.txt 42B
image1608_png.rf.0788db0295df7723c3724793c4e5dbe8.txt 42B
image2048_png.rf.0a7f02c6760a4236c2be2fbcd47bafe1.txt 42B
image803_png.rf.7aa399f0feca2e79048265e78eae7d87.txt 42B
image947_png.rf.352659c6dce697d0b3d8ec551aa73b28.txt 42B
image3051_png.rf.34c4a827d6ef54192383611b69b0d66b.txt 42B
image717_png.rf.e37d278ecfacdfbf1926d008ec502d66.txt 42B
image2332_png.rf.45fe9ea212fcc9bdd03fa931ef50474a.txt 42B
image2096_png.rf.c31c3d4fbe324e60c650faf56293e63e.txt 42B
image2825_png.rf.13e716129ba4a1a7f708b8293eb43d15.txt 42B
image1316_png.rf.098656525607fc43e0840d224a4a970c.txt 42B
image2678_png.rf.c23904a0fc620f258760aab82ffcef79.txt 42B
image808_png.rf.38a5d54485b72f061783de4529c6dfc1.txt 42B
image2871_png.rf.da860c9f15175ad99981077b0ee3840a.txt 42B
image1218_png.rf.3739272c78246d51f87926501a9516db.txt 42B
image1999_png.rf.beac5abefed27cea897ef32e74a05867.txt 42B
image2794_png.rf.636ae4b37da9d73e8e16da380d85b7e9.txt 42B
image2373_png.rf.ee4b3eedef90cfa3a157249bdd21eca4.txt 42B
image2103_png.rf.a91741107335913326a196cdfa6ce09d.txt 42B
image947_png.rf.dde1a2d88781adad0594ad1a213f9549.txt 42B
image594_png.rf.d6b6329f57de1cd5ffefe025ad8819cc.txt 42B
image1394_png.rf.0d03cc92eb7aa1eff355a475145804ac.txt 42B
image2952_png.rf.10d3808b31323e6787ecf011b9280cc4.txt 42B
image1441_png.rf.75a5e4da683df3f07f74651c0ed81666.txt 42B
image1340_png.rf.bcd4b0b9d61688ded675e99442224cfa.txt 42B
image2031_png.rf.167c3803a8a880efa78c56cd27dedc1c.txt 42B
image1383_png.rf.22dda5bace9aa80d77dd9f3cd693959d.txt 42B
image2313_png.rf.bbab94a28bacf201c0d064442698a749.txt 42B
image2421_png.rf.24e60e3c423ea477923b8334754f7772.txt 42B
image2616_png.rf.6b64542854ba9914330c33da57c841bc.txt 42B
image1869_png.rf.f4441eeec9a8edf54c707899dbbdf759.txt 42B
image2678_png.rf.8e1d5300df985599ab6f8a34f462d2b6.txt 42B
共 2000 条
- 1
- 2
- 3
- 4
- 5
- 6
- 20
资源评论
stsdddd
- 粉丝: 2w+
- 资源: 710
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