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, [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>
## 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
【资源说明】 基于YOLOV5+DeepSort算法进行地下空间中的行人平均速度检测源码+训练好的模型+数据集+操作使用说明(高分项目)基于YOLOV5+DeepSort算法进行地下空间中的行人平均速度检测源码+训练好的模型+数据集+操作使用说明(高分项目)基于YOLOV5+DeepSort算法进行地下空间中的行人平均速度检测源码+训练好的模型+数据集+操作使用说明(高分项目)基于YOLOV5+DeepSort算法进行地下空间中的行人平均速度检测源码+训练好的模型+数据集+操作使用说明(高分项目) 【备注】 1、该项目是个人高分毕业设计项目源码,已获导师指导认可通过,答辩评审分达到95分 2、该资源内项目代码都经过测试运行成功,功能ok的情况下才上传的,请放心下载使用! 3、本项目适合计算机相关专业(如软件工程、计科、人工智能、通信工程、自动化、电子信息等)的在校学生、老师或者企业员工下载使用,也可作为毕业设计、课程设计、作业、项目初期立项演示等,当然也适合小白学习进阶。 4、如果基础还行,可以在此代码基础上进行修改,以实现其他功能,也可直接用于毕设、课设、作业等。 欢迎下载,沟通交流,互相学习,共同进步!
资源推荐
资源详情
资源评论
收起资源包目录
基于YOLOV5+DeepSort算法进行地下空间中的行人平均速度检测源码+训练好的模型+数据集+操作使用说明(高分项目) (299个子文件)
setup.cfg 2KB
.isort.cfg 307B
gnn_propagate.cpp 693B
build_adjacency_matrix.cpp 640B
gnn_propagate_kernel.cu 1KB
build_adjacency_matrix_kernel.cu 1KB
Dockerfile 2KB
Dockerfile 821B
Dockerfile-arm64 2KB
Dockerfile-cpu 2KB
.dockerignore 4KB
.flake8 496B
track_all.gif 7.83MB
track_pedestrians.gif 3.63MB
.gitignore 2KB
.gitignore 176B
.gitignore 89B
.gitkeep 0B
YOLOV5_DeepSort.iml 632B
tutorial.ipynb 56KB
ranking_results.jpg 186KB
actmap.jpg 29KB
LICENSE 1KB
LICENSE 1KB
Makefile 580B
Makefile 101B
MODEL_ZOO.md 11KB
README.md 11KB
AWESOME_REID.md 7KB
README.md 5KB
README.md 2KB
README.md 2KB
README.md 1KB
README.md 806B
README.md 665B
README.md 424B
yolov5m.pt 40.28MB
osnet_x1_0_msmt17.pth 10.49MB
osnet_x1_0_msmt17.pth 10.49MB
osnet_x0_25_msmt17.pth 2.92MB
osnet_x0_25_msmt17.pth 2.92MB
osnet_x0_25_market1501.pth 2.35MB
osnet_x0_25_market1501.pth 2.35MB
dataloaders.py 46KB
general.py 41KB
nasnet.py 35KB
common.py 35KB
train.py 34KB
export.py 29KB
wandb_utils.py 27KB
tf.py 25KB
plots.py 21KB
senet.py 20KB
datamanager.py 20KB
track.py 19KB
val.py 19KB
osnet_search.py 18KB
osnet_ain.py 17KB
engine.py 17KB
osnet.py 17KB
osnet_child.py 16KB
dataset.py 16KB
yolo.py 15KB
resnet.py 15KB
metrics.py 14KB
hacnn.py 13KB
torch_utils.py 13KB
detect.py 13KB
main.py 12KB
cuhk03.py 12KB
augmentations.py 12KB
json_logger.py 11KB
densenet.py 11KB
radam.py 11KB
osnet.py 11KB
inceptionv4.py 11KB
inceptionresnetv2.py 11KB
transforms.py 10KB
track.py 10KB
loss.py 10KB
xception.py 9KB
torchtools.py 9KB
model_complexity.py 9KB
resnetmid.py 9KB
pcb.py 9KB
resnet_ibn_a.py 8KB
mlfn.py 8KB
sampler.py 8KB
mobilenetv2.py 8KB
default_config.py 8KB
resnet_ibn_b.py 8KB
kalman_filter.py 8KB
__init__.py 8KB
shufflenetv2.py 8KB
default_config.py 8KB
default_config.py 8KB
tracker.py 8KB
linear_assignment.py 7KB
squeezenet.py 7KB
autoanchor.py 7KB
共 299 条
- 1
- 2
- 3
资源评论
- HOOOOORAY!!!!!2024-05-05果断支持这个资源,资源解决了当前遇到的问题,给了新的灵感,感谢分享~
不走小道
- 粉丝: 3209
- 资源: 5120
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