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<
没有合适的资源?快使用搜索试试~ 我知道了~
利用DALLE-2模型将文本转图像,并且通过yolov5自动检测.zip
共179个文件
pyc:45个
py:45个
png:36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 197 浏览量
2023-12-24
09:49:19
上传
评论
收藏 41.52MB ZIP 举报
温馨提示
图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测 图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测 图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测 图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测 图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测 图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测 图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测 图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测 图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测 图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测 图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测 图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测图像检测 图像检测图像检
资源推荐
资源详情
资源评论
收起资源包目录
利用DALLE-2模型将文本转图像,并且通过yolov5自动检测.zip (179个子文件)
Dockerfile 821B
.gitattributes 66B
.gitignore 47B
project.iml 385B
config.ini 54B
logo.jpeg 14KB
background.jpg 181KB
OpenAI-Dall-E-2.jpg 44KB
setting.json 78B
fold.json 66B
ip.json 52B
app.log 0B
README.md 11KB
README.md 2KB
README.md 225B
ss.md 37B
generated_image_1679892065.png 3MB
generated_image_1679893014.png 3MB
generated_image_1679893068.png 3MB
generated_image_1679892029.png 3MB
generated_image_1679845776.png 3MB
generated_image_1679893166.png 3MB
背景.png 2.39MB
图片1.png 101KB
openai_big.png 68KB
运行.png 9KB
赞停.png 6KB
conan.png 4KB
表情.png 4KB
数据探索.png 4KB
openai.png 3KB
停止.png 3KB
摄像头关.png 3KB
doctor.png 3KB
evil.png 3KB
摄像头开.png 3KB
实时视频流解析.png 2KB
button-on.png 2KB
暂停.png 2KB
打开.png 2KB
终止.png 1KB
button-off.png 1KB
圆.png 1KB
笑脸.png 786B
正方形.png 718B
箭头_列表展开.png 668B
箭头_列表收起.png 645B
关闭.png 605B
还原.png 601B
下拉_白色.png 573B
最大化.png 406B
最小化.png 249B
yolov5s.pt 14.12MB
apprcc_rc.py 11.56MB
win.py 67KB
datasets.py 45KB
general.py 36KB
common.py 32KB
wandb_utils.py 27KB
tf.py 20KB
plots.py 20KB
wandb_utils.py 19KB
main.py 17KB
yolo.py 15KB
metrics.py 14KB
torch_utils.py 14KB
augmentations.py 11KB
DetThread.py 10KB
loss.py 9KB
__init__.py 7KB
autoanchor.py 7KB
downloads.py 6KB
google_utils.py 6KB
experimental.py 4KB
benchmarks.py 4KB
activations.py 4KB
callbacks.py 2KB
autobatch.py 2KB
TaskThread.py 2KB
CustomMessageBox.py 2KB
ProgressDialogWidget.py 1KB
resume.py 1KB
sweep.py 1KB
__init__.py 1KB
restapi.py 1KB
log_dataset.py 1KB
sweep.py 877B
log_dataset.py 870B
capnums.py 643B
GetImageURL.py 636B
MouseLabel.py 574B
Logger.py 326B
example_request.py 299B
cal_fps.py 0B
__init__.py 0B
__init__.py 0B
__init__.py 0B
__init__.py 0B
apprcc_rc.cpython-39.pyc 2.81MB
apprcc_rc.cpython-38.pyc 2.72MB
共 179 条
- 1
- 2
资源评论
天天501
- 粉丝: 603
- 资源: 4666
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