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)
* [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, [email protected], [email protected]: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>
## 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. 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> 2: 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>
<summary> <b>Usage</b> </summary>
<b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --data .. --upload_data </code>
![Screenshot (72)](https://user-images.githubusercontent.com/15766192/128979739-4cf63aeb-a76f-483f-8861-1c0100b938a5.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 utils/logger/wandb/log_dataset.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 from <code>_wandb.yaml</code> file and set <code>--save_period</code>
<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>
</details>
<h3> Reports </h3>
W&B Repor
没有合适的资源?快使用搜索试试~ 我知道了~
Python基于行人重识别的密切接触者识别及示踪系统源码.zip
共191个文件
pyc:85个
py:62个
yaml:24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
5星 · 超过95%的资源 2 下载量 121 浏览量
2022-06-15
23:09:08
上传
评论
收藏 771KB ZIP 举报
温馨提示
Python基于行人重识别的密切接触者识别及示踪系统源码.zip
资源推荐
资源详情
资源评论
收起资源包目录
Python基于行人重识别的密切接触者识别及示踪系统源码.zip (191个子文件)
style.css 2KB
Dockerfile 821B
index.html 6KB
bg2.jpg 111KB
query.jpg 80KB
0001_c1s1_0_489.jpg 80KB
loadimage.js 5KB
LICENSE 1KB
README.md 10KB
README.md 2KB
datasets.py 45KB
general.py 34KB
common.py 31KB
common1.py 27KB
utilsReid.py 26KB
wandb_utils.py 25KB
detect.py 22KB
tf.py 20KB
plots.py 20KB
yolo.py 15KB
metrics.py 13KB
torch_utils.py 13KB
augmentations.py 11KB
loss.py 9KB
autoanchor.py 7KB
__init__.py 7KB
downloads.py 6KB
goto.py 6KB
resnet_ibn_a.py 5KB
views.py 5KB
experimental.py 4KB
resnet.py 4KB
market1501.py 4KB
activations.py 4KB
defaults.py 3KB
settings.py 3KB
baseline.py 2KB
callbacks.py 2KB
autobatch.py 2KB
eval_reid.py 2KB
transforms.py 2KB
bases.py 2KB
resume.py 1KB
dataset_loader.py 1KB
sweep.py 1KB
urls.py 1KB
restapi.py 1KB
log_dataset.py 1KB
build.py 808B
0001_initial.py 695B
manage.py 543B
__init__.py 458B
build.py 454B
__init__.py 427B
models.py 425B
wsgi.py 399B
collate_batch.py 373B
example_request.py 299B
__init__.py 158B
__init__.py 117B
__init__.py 117B
__init__.py 113B
__init__.py 83B
apps.py 82B
admin.py 63B
tests.py 60B
__init__.py 0B
__init__.py 0B
__init__.py 0B
__init__.py 0B
__init__.py 0B
__init__.py 0B
datasets.cpython-36.pyc 36KB
datasets.cpython-37.pyc 35KB
datasets.cpython-38.pyc 35KB
common.cpython-36.pyc 31KB
common.cpython-38.pyc 31KB
general.cpython-36.pyc 30KB
general.cpython-38.pyc 30KB
general.cpython-37.pyc 30KB
plots.cpython-36.pyc 18KB
plots.cpython-37.pyc 18KB
plots.cpython-38.pyc 18KB
utilsReid.cpython-36.pyc 17KB
utilsReid.cpython-38.pyc 17KB
yolo.cpython-36.pyc 13KB
yolo.cpython-38.pyc 13KB
torch_utils.cpython-38.pyc 12KB
torch_utils.cpython-36.pyc 12KB
torch_utils.cpython-37.pyc 12KB
metrics.cpython-38.pyc 11KB
metrics.cpython-36.pyc 11KB
metrics.cpython-37.pyc 11KB
augmentations.cpython-38.pyc 9KB
detect.cpython-36.pyc 9KB
augmentations.cpython-36.pyc 9KB
augmentations.cpython-37.pyc 9KB
autoanchor.cpython-36.pyc 6KB
autoanchor.cpython-38.pyc 6KB
resnet_ibn_a.cpython-36.pyc 5KB
共 191 条
- 1
- 2
资源评论
- seeseeseesea2023-04-21资源值得借鉴的内容很多,那就浅学一下吧,值得下载!
- Femirins5302023-03-06发现一个宝藏资源,资源有很高的参考价值,赶紧学起来~
「已注销」
- 粉丝: 802
- 资源: 3611
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