# ClearML Integration
<img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_dark.png#gh-light-mode-only" alt="Clear|ML"><img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_light.png#gh-dark-mode-only" alt="Clear|ML">
## About ClearML
[ClearML](https://cutt.ly/yolov5-tutorial-clearml) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time â±ï¸.
ð¨ Track every YOLOv5 training run in the <b>experiment manager</b>
ð§ Version and easily access your custom training data with the integrated ClearML <b>Data Versioning Tool</b>
ð¦ <b>Remotely train and monitor</b> your YOLOv5 training runs using ClearML Agent
ð¬ Get the very best mAP using ClearML <b>Hyperparameter Optimization</b>
ð Turn your newly trained <b>YOLOv5 model into an API</b> with just a few commands using ClearML Serving
<br />
And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline!
<br />
<br />
![ClearML scalars dashboard](https://github.com/thepycoder/clearml_screenshots/raw/main/experiment_manager_with_compare.gif)
<br />
<br />
## 𦾠Setting Things Up
To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one:
Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-tutorial-clearml) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go!
1. Install the `clearml` python package:
```bash
pip install clearml
```
1. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions:
```bash
clearml-init
```
That's it! You're done ð
<br />
## ð Training YOLOv5 With ClearML
To enable ClearML experiment tracking, simply install the ClearML pip package.
```bash
pip install clearml>=1.2.0
```
This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager.
If you want to change the `project_name` or `task_name`, use the `--project` and `--name` arguments of the `train.py` script, by default the project will be called `YOLOv5` and the task `Training`.
PLEASE NOTE: ClearML uses `/` as a delimiter for subprojects, so be careful when using `/` in your project name!
```bash
python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
```
or with custom project and task name:
```bash
python train.py --project my_project --name my_training --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
```
This will capture:
- Source code + uncommitted changes
- Installed packages
- (Hyper)parameters
- Model files (use `--save-period n` to save a checkpoint every n epochs)
- Console output
- Scalars (mAP_0.5, mAP_0.5:0.95, precision, recall, losses, learning rates, ...)
- General info such as machine details, runtime, creation date etc.
- All produced plots such as label correlogram and confusion matrix
- Images with bounding boxes per epoch
- Mosaic per epoch
- Validation images per epoch
- ...
That's a lot right? ð¤¯
Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them!
There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works!
<br />
## ð Dataset Version Management
Versioning your data separately from your code is generally a good idea and makes it easy to acquire the latest version too. This repository supports supplying a dataset version ID, and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment!
![ClearML Dataset Interface](https://github.com/thepycoder/clearml_screenshots/raw/main/clearml_data.gif)
### Prepare Your Dataset
The YOLOv5 repository supports a number of different datasets by using yaml files containing their information. By default datasets are downloaded to the `../datasets` folder in relation to the repository root folder. So if you downloaded the `coco128` dataset using the link in the yaml or with the scripts provided by yolov5, you get this folder structure:
```
..
|_ yolov5
|_ datasets
|_ coco128
|_ images
|_ labels
|_ LICENSE
|_ README.txt
```
But this can be any dataset you wish. Feel free to use your own, as long as you keep to this folder structure.
Next, â ï¸**copy the corresponding yaml file to the root of the dataset folder**â ï¸. This yaml files contains the information ClearML will need to properly use the dataset. You can make this yourself too, of course, just follow the structure of the example yamls.
Basically we need the following keys: `path`, `train`, `test`, `val`, `nc`, `names`.
```
..
|_ yolov5
|_ datasets
|_ coco128
|_ images
|_ labels
|_ coco128.yaml # <---- HERE!
|_ LICENSE
|_ README.txt
```
### Upload Your Dataset
To get this dataset into ClearML as a versioned dataset, go to the dataset root folder and run the following command:
```bash
cd coco128
clearml-data sync --project YOLOv5 --name coco128 --folder .
```
The command `clearml-data sync` is actually a shorthand command. You could also run these commands one after the other:
```bash
# Optionally add --parent <parent_dataset_id> if you want to base
# this version on another dataset version, so no duplicate files are uploaded!
clearml-data create --name coco128 --project YOLOv5
clearml-data add --files .
clearml-data close
```
### Run Training Using A ClearML Dataset
Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 ð models!
```bash
python train.py --img 640 --batch 16 --epochs 3 --data clearml://<your_dataset_id> --weights yolov5s.pt --cache
```
<br />
## ð Hyperparameter Optimization
Now that we have our experiments and data versioned, it's time to take a look at what we can build on top!
Using the code information, installed packages and environment details, the experiment itself is now **completely reproducible**. In fact, ClearML allows you to clone an experiment and even change its parameters. We can then just rerun it with these new parameters automatically, this is basically what HPO does!
To **run hyperparameter optimization locally**, we've included a pre-made script for you. Just make sure a training task has been run at least once, so it is in the ClearML experiment manager, we will essentially clone it and change its hyperparameters.
You'll need to fill in the ID of this `template task` in the script found at `utils/loggers/clearml/hpo.py` and then just run it :) You can change `task.execute_locally()` to `task.execute()` to put it in a ClearML queue and have a remote agent work on it instead.
```bash
# To use optuna, install it first, otherwise you can change the optimizer to just be RandomSearch
pip install optuna
python utils/loggers/clearml/hpo.py
```
![HPO](https://github.com/thepycoder/clearml_screenshots/raw/main/hpo.png)
## 𤯠Remote Execution (advanced)
Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access t
没有合适的资源?快使用搜索试试~ 我知道了~
基于Yolov5的烟雾车辆探测器
共483个文件
jpg:301个
txt:98个
py:42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 173 浏览量
2024-05-21
20:23:35
上传
评论
收藏 136.99MB ZIP 举报
温馨提示
烟雾车辆探测器 基于Yolov5的烟雾车辆探测器 要求:pytorch、opencv、numpy等
资源推荐
资源详情
资源评论
收起资源包目录
基于Yolov5的烟雾车辆探测器 (483个子文件)
val.cache 20KB
Dockerfile 3KB
Dockerfile 821B
Dockerfile-arm64 2KB
Dockerfile-cpu 2KB
ico.ico 259KB
image587.jpg 1.36MB
image456.jpg 1.17MB
image456.jpg 1.17MB
image440.jpg 1.12MB
image440.jpg 1.12MB
image439.jpg 1.1MB
image439.jpg 1.1MB
image384.jpg 1.1MB
image384.jpg 1.1MB
image390.jpg 1.05MB
image390.jpg 1.05MB
image304.jpg 1.04MB
image186.jpg 1017KB
image344.jpg 1008KB
image344.jpg 1008KB
image939.jpg 990KB
image939.jpg 990KB
image406.jpg 974KB
image406.jpg 974KB
image436.jpg 965KB
image436.jpg 965KB
image401.jpg 948KB
image401.jpg 948KB
image958.jpg 930KB
image375.jpg 927KB
image375.jpg 927KB
image359.jpg 914KB
image359.jpg 914KB
image351.jpg 914KB
image351.jpg 914KB
image315.jpg 903KB
image315.jpg 903KB
image311.jpg 900KB
image311.jpg 900KB
image929.jpg 881KB
image929.jpg 881KB
image330.jpg 870KB
image330.jpg 870KB
image824.jpg 869KB
image824.jpg 869KB
image339.jpg 830KB
image339.jpg 830KB
image807.jpg 823KB
image767.jpg 819KB
image767.jpg 819KB
image301.jpg 808KB
image301.jpg 808KB
image833.jpg 805KB
image947.jpg 783KB
image947.jpg 783KB
image770.jpg 747KB
image770.jpg 747KB
image761.jpg 741KB
image761.jpg 741KB
image728.jpg 710KB
image831.jpg 705KB
image831.jpg 705KB
image542.jpg 701KB
image832.jpg 684KB
image75.jpg 682KB
image75.jpg 682KB
image575.jpg 655KB
image783.jpg 630KB
image925.jpg 621KB
image925.jpg 621KB
image981.jpg 616KB
image981.jpg 616KB
image983.jpg 616KB
image983.jpg 616KB
image805.jpg 614KB
image745.jpg 597KB
image745.jpg 597KB
image973.jpg 596KB
image973.jpg 596KB
image970.jpg 582KB
image970.jpg 582KB
image842.jpg 582KB
image842.jpg 582KB
image137.jpg 571KB
image137.jpg 571KB
image738.jpg 568KB
image738.jpg 568KB
image772.jpg 560KB
image990.jpg 551KB
image990.jpg 551KB
image131.jpg 546KB
image131.jpg 546KB
image127.jpg 542KB
image127.jpg 542KB
image546.jpg 541KB
image980.jpg 539KB
image108.jpg 536KB
image108.jpg 536KB
image747.jpg 534KB
共 483 条
- 1
- 2
- 3
- 4
- 5
资源评论
hakesashou
- 粉丝: 4397
- 资源: 1166
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