馃摎 This guide explains how to use **Weights & Biases** (W&B) with YOLOv5 馃殌. UPDATED 29 September 2021.
- [About Weights & Biases](#about-weights-&-biases)
- [First-Time Setup](#first-time-setup)
- [Viewing runs](#viewing-runs)
- [Disabling wandb](#disabling-wandb)
- [Advanced Usage: Dataset Versioning and Evaluation](#advanced-usage)
- [Reports: Share your work with the world!](#reports)
## About Weights & Biases
Think of [W&B](https://wandb.ai/site?utm_campaign=repo_yolo_wandbtutorial) like GitHub for machine learning models. With a few lines of code, save everything you need to debug, compare and reproduce your models 鈥� architecture, hyperparameters, git commits, model weights, GPU usage, and even datasets and predictions.
Used by top researchers including teams at OpenAI, Lyft, Github, and MILA, W&B is part of the new standard of best practices for machine learning. How W&B can help you optimize your machine learning workflows:
- [Debug](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Free-2) model performance in real time
- [GPU usage](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#System-4) visualized automatically
- [Custom charts](https://wandb.ai/wandb/customizable-charts/reports/Powerful-Custom-Charts-To-Debug-Model-Peformance--VmlldzoyNzY4ODI) for powerful, extensible visualization
- [Share insights](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Share-8) interactively with collaborators
- [Optimize hyperparameters](https://docs.wandb.com/sweeps) efficiently
- [Track](https://docs.wandb.com/artifacts) datasets, pipelines, and production models
## First-Time Setup
<details open>
<summary> Toggle Details </summary>
When you first train, W&B will prompt you to create a new account and will generate an **API key** for you. If you are an existing user you can retrieve your key from https://wandb.ai/authorize. This key is used to tell W&B where to log your data. You only need to supply your key once, and then it is remembered on the same device.
W&B will create a cloud **project** (default is 'YOLOv5') for your training runs, and each new training run will be provided a unique run **name** within that project as project/name. You can also manually set your project and run name as:
```shell
$ python train.py --project ... --name ...
```
YOLOv5 notebook example: <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>
<img width="960" alt="Screen Shot 2021-09-29 at 10 23 13 PM" src="https://user-images.githubusercontent.com/26833433/135392431-1ab7920a-c49d-450a-b0b0-0c86ec86100e.png">
</details>
## Viewing Runs
<details open>
<summary> Toggle Details </summary>
Run information streams from your environment to the W&B cloud console as you train. This allows you to monitor and even cancel runs in <b>realtime</b> . All important information is logged:
- Training & Validation losses
- Metrics: Precision, Recall, mAP@0.5, mAP@0.5:0.95
- Learning Rate over time
- A bounding box debugging panel, showing the training progress over time
- GPU: Type, **GPU Utilization**, power, temperature, **CUDA memory usage**
- System: Disk I/0, CPU utilization, RAM memory usage
- Your trained model as W&B Artifact
- Environment: OS and Python types, Git repository and state, **training command**
<p align="center"><img width="900" alt="Weights & Biases dashboard" src="https://user-images.githubusercontent.com/26833433/135390767-c28b050f-8455-4004-adb0-3b730386e2b2.png"></p>
</details>
## Disabling wandb
- training after running `wandb disabled` inside that directory creates no wandb run
![Screenshot (84)](https://user-images.githubusercontent.com/15766192/143441777-c780bdd7-7cb4-4404-9559-b4316030a985.png)
- To enable wandb again, run `wandb online`
![Screenshot (85)](https://user-images.githubusercontent.com/15766192/143441866-7191b2cb-22f0-4e0f-ae64-2dc47dc13078.png)
## Advanced Usage
You can leverage W&B artifacts and Tables integration to easily visualize and manage your datasets, models and training evaluations. Here are some quick examples to get you started.
<details open>
<h3> 1: Train and Log Evaluation simultaneousy </h3>
This is an extension of the previous section, but it'll also training after uploading the dataset. <b> This also evaluation Table</b>
Evaluation table compares your predictions and ground truths across the validation set for each epoch. It uses the references to the already uploaded datasets,
so no images will be uploaded from your system more than once.
<details open>
<summary> <b>Usage</b> </summary>
<b>Code</b> <code> $ python train.py --upload_data val</code>
![Screenshot from 2021-11-21 17-40-06](https://user-images.githubusercontent.com/15766192/142761183-c1696d8c-3f38-45ab-991a-bb0dfd98ae7d.png)
</details>
<h3>2. Visualize and Version Datasets</h3>
Log, visualize, dynamically query, and understand your data with <a href='https://docs.wandb.ai/guides/data-vis/tables'>W&B Tables</a>. You can use the following command to log your dataset as a W&B Table. This will generate a <code>{dataset}_wandb.yaml</code> file which can be used to train from dataset artifact.
<details>
<summary> <b>Usage</b> </summary>
<b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data .. </code>
![Screenshot (64)](https://user-images.githubusercontent.com/15766192/128486078-d8433890-98a3-4d12-8986-b6c0e3fc64b9.png)
</details>
<h3> 3: Train using dataset artifact </h3>
When you upload a dataset as described in the first section, you get a new config file with an added `_wandb` to its name. This file contains the information that
can be used to train a model directly from the dataset artifact. <b> This also logs evaluation </b>
<details>
<summary> <b>Usage</b> </summary>
<b>Code</b> <code> $ python train.py --data {data}_wandb.yaml </code>
![Screenshot (72)](https://user-images.githubusercontent.com/15766192/128979739-4cf63aeb-a76f-483f-8861-1c0100b938a5.png)
</details>
<h3> 4: Save model checkpoints as artifacts </h3>
To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval.
You can also log both the dataset and model checkpoints simultaneously. If not passed, only the final model will be logged
<details>
<summary> <b>Usage</b> </summary>
<b>Code</b> <code> $ python train.py --save_period 1 </code>
![Screenshot (68)](https://user-images.githubusercontent.com/15766192/128726138-ec6c1f60-639d-437d-b4ee-3acd9de47ef3.png)
</details>
</details>
<h3> 5: Resume runs from checkpoint artifacts. </h3>
Any run can be resumed using artifacts if the <code>--resume</code> argument starts with聽<code>wandb-artifact://</code>聽prefix followed by the run path, i.e,聽<code>wandb-artifact://username/project/runid </code>. This doesn't require the model checkpoint to be present on the local system.
<details>
<summary> <b>Usage</b> </summary>
<b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code>
![Screenshot (70)](https://user-images.githubusercontent.com/15766192/128728988-4e84b355-6c87-41ae-a591-14aecf45343e.png)
</details>
<h3> 6: Resume runs from dataset artifact & checkpoint artifacts. </h3>
<b> Local dataset or model checkpoints are not required. This can be used to resume runs directly on a different device </b>
The syntax is same as the previous section, but you'll need to lof both the dataset and model checkpoints as artifacts, i.e, set bot <code>--upload_dataset</code> or
train fro
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
目前机器学习的替代者深度学习(卷积神经网络)基于卷积神经网络,设计一个基于深度学习的车牌检测识别系统,用于本科毕业论文可以说是非常合适的,这篇博文记录的就是一篇本科毕业设计所用到的模型算法,训练这个代码(在RTX4090上训练中等参数的Model大概只需要2个小时(时长不止由显卡配置决定,还取决于你选择的参数大小)),只需要提供你想检测的包含车牌的图片(任何位置、角度都可以),就可以得到图片中车牌位置的标记和车牌号码的输出,如果你有摄像头,你可以通过训练得到的模型去调用摄像头,通过摄像头动态监测车牌,也可以把一段.mp4结尾的视频丢给模型。模型会给你返回用框框标记好的图片以及输出检测出的车牌号码)
资源推荐
资源详情
资源评论
收起资源包目录
基于深度学习的车牌识别算法,其中,车辆检测网络直接使用YOLO侦测.zip (252个子文件)
Dockerfile 2KB
Dockerfile 821B
Dockerfile-arm64 2KB
Dockerfile-cpu 1KB
.dockerignore 4KB
.gitignore 1KB
.gitignore 176B
.gitignore 47B
LicensePlate-master.iml 580B
fake_chs_lp-master.iml 284B
13.jpeg 68KB
2.jpg 408KB
14.jpg 160KB
10.jpg 152KB
11.jpg 143KB
1.jpg 96KB
3.jpg 94KB
9.jpg 49KB
8.jpg 47KB
1.jpg 29KB
5.jpg 29KB
6.jpg 18KB
LICENSE 1KB
README.md 11KB
README.md 2KB
README.md 366B
smu.png 3.47MB
4.png 2.61MB
1.png 1.22MB
2.png 996KB
7.png 902KB
12.png 626KB
yellow_bg.png 28KB
blue_bg.png 27KB
black_bg.png 23KB
green_bg_0.png 4KB
green_bg_1.png 3KB
ne015.png 382B
ne013.png 357B
ne004.png 338B
ne116.png 327B
ne021.png 320B
ne025.png 313B
ne026.png 311B
ne010.png 310B
ne017.png 303B
ne133.png 303B
ne007.png 300B
ne121.png 298B
ne002.png 290B
ne001.png 288B
ne030.png 287B
ne018.png 284B
ne009.png 283B
ne130.png 279B
ne008.png 278B
ne020.png 273B
ne109.png 271B
ne101.png 268B
ne106.png 263B
ne016.png 263B
ne127.png 258B
ne123.png 258B
ne014.png 256B
ne132.png 253B
ne126.png 252B
ne129.png 252B
ne023.png 248B
ne115.png 248B
ne011.png 247B
ne005.png 236B
ne024.png 234B
ne012.png 233B
ne114.png 232B
ne000.png 232B
ne128.png 228B
ne122.png 222B
ne100.png 217B
ne112.png 215B
ne003.png 214B
ne028.png 207B
ne119.png 206B
ne102.png 201B
ne120.png 200B
ne019.png 197B
ne111.png 197B
ne113.png 193B
ne103.png 193B
ne131.png 191B
ne104.png 180B
ne124.png 178B
ne006.png 172B
ne027.png 170B
ne022.png 160B
ne029.png 158B
ne108.png 157B
ne125.png 148B
ne118.png 146B
ne105.png 143B
ne107.png 130B
共 252 条
- 1
- 2
- 3
资源评论
博士僧小星
- 粉丝: 1722
- 资源: 5850
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