Ultralytics Code Refactor https://ultralytics.com/actions (#13301)

* Refactor code for speed and clarity

* Auto-format by https://ultralytics.com/actions

* Update README.md

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update README.zh-CN.md

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Auto-format by https://ultralytics.com/actions

* Update README.md

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Auto-format by https://ultralytics.com/actions

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

utils/loggers/clearml/README.md

@@ -4,7 +4,7 @@
 
 ## About ClearML
 
-[ClearML](https://cutt.ly/yolov5-tutorial-clearml) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️.
+[ClearML](https://clear.ml) 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>
 
@@ -18,13 +18,13 @@
 
 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!
 
-![ClearML scalars dashboard](https://github.com/thepycoder/clearml_screenshots/raw/main/experiment_manager_with_compare.gif)
+![ClearML scalars dashboard](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/experiment_manager_with_compare.gif)
 
 ## 🦾 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!
+Either sign up for free to the [ClearML Hosted Service](https://clear.ml) 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:
 
@@ -85,7 +85,7 @@ There even more we can do with all of this information, like hyperparameter opti
 
 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)
+![ClearML Dataset Interface](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/clearml_data.gif)
 
 ### Prepare Your Dataset
 
@@ -163,13 +163,13 @@ pip install optuna
 python utils/loggers/clearml/hpo.py
 ```
 
-![HPO](https://github.com/thepycoder/clearml_screenshots/raw/main/hpo.png)
+![HPO](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/hpo.png)
 
 ## 🤯 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 to a very powerful GPU machine on-site, or you have some budget to use cloud GPUs. This is where the ClearML Agent comes into play. Check out what the agent can do here:
 
-- [YouTube video](https://youtu.be/MX3BrXnaULs)
+- [YouTube video](https://www.youtube.com/watch?v=MX3BrXnaULs&feature=youtu.be)
 - [Documentation](https://clear.ml/docs/latest/docs/clearml_agent)
 
 In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc. to the experiment manager.
@@ -190,7 +190,7 @@ With our agent running, we can give it some work. Remember from the HPO section
 
 ⏳ Enqueue the task to any of the queues by right-clicking it
 
-![Enqueue a task from the UI](https://github.com/thepycoder/clearml_screenshots/raw/main/enqueue.gif)
+![Enqueue a task from the UI](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/enqueue.gif)
 
 ### Executing A Task Remotely