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---------

Signed-off-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

utils/loggers/clearml/README.md

@@ -45,7 +45,7 @@ That's it! You're done 😎
 To enable ClearML experiment tracking, simply install the ClearML pip package.
 
 ```bash
-pip install clearml>=1.2.0
+pip install clearml
 ```
 
 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.
@@ -144,7 +144,7 @@ clearml-data close
 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
+python train.py --img 640 --batch 16 --epochs 3 --data clearml:// yolov5s.pt --cache YOUR_DATASET_ID --weights
 ```
 
 ## 👀 Hyperparameter Optimization
@@ -177,7 +177,7 @@ In short: every experiment tracked by the experiment manager contains enough inf
 You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running:
 
 ```bash
-clearml-agent daemon --queue <queues_to_listen_to> [--docker]
+clearml-agent daemon --queue QUEUES_TO_LISTEN_TO [--docker]
 ```
 
 ### Cloning, Editing And Enqueuing