基于Vue+ElementUI+Flask+Mysql/Sqlite+Yolov5(Pytorch)技术栈实现的智慧农场系统

# 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 ⏱️. 🔨 Track every YOLOv5 training run in the <b>experiment manager</b> 🔧 Version and easily access your custom training data with the integrated ClearML <b>Data Versioning Tool</b> 🔦 <b>Remotely train and monitor</b> your YOLOv5 training runs using ClearML Agent 🔬 Get the very best mAP using ClearML <b>Hyperparameter Optimization</b> 🔭 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 />  <br /> <br /> ## 🦾 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 😎 <br /> ## 🚀 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 delimter 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? 🤯 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 /> ## 🔗 Dataset Version Management Versioning your data separately from your code is generally a good idea and makes it easy to aqcuire 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!  ### 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, ⚠️**copy the corresponding yaml file to the root of the dataset folder**⚠️. 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 versionned 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 🚀 models! ```bash python train.py --img 640 --batch 16 --epochs 3 --data clearml://<your_dataset_id> --weights yolov5s.pt --cache ``` <br /> ## 👀 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 ```  ## 🤯 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