@@ -1,4 +1,4 @@
-# Third-party experiment monitoring
+# Third-party Experiment Monitoring
 
 SuperGradients supports out-of-the-box Weights & Biases (wandb) and ClearML. 
 You can also inherit from our base class to integrate any monitoring tool with minimal code change.  
@@ -11,6 +11,7 @@ Tensorboard is natively integrated into the training and validation steps. You c
 
 ### Weights & Biases
 **requirements**:
+
 - Install `wandb`
 - Set up wandb according to the [official documentation](https://docs.wandb.ai/quickstart#1.-set-up-wandb)
 - Make sure to login (You can check if you have a `~/.netrc` token)
@@ -41,7 +42,8 @@ trainer.train(model=model, training_params=training_params, ...)
 
 
 ### ClearML
-**requirements**:
+**requirements**
+
 - Install `clearml` 
 - Set up CleaML according to the [official documentation](https://clear.ml/docs/latest/docs/getting_started/ds/ds_first_steps#install-clearml)
 - Adapt your code like in the following example
@@ -151,10 +153,11 @@ training_params = {
 trainer.train(model=model, training_params=training_params, ...)
 ```
 
-**Notes**:
- - `@multi_process_safe` prevents multiple training nodes to do the same action. Check out [DDP documentation](device.md) for more details
- - `@register_logger()` registers your class into our factory, allowing it to be instantiated from a string.
- - `sg_logger_params` only requires `project_name`, the rest is provided by the Trainer.
+**Notes**
+
+- `@multi_process_safe` prevents multiple training nodes to do the same action. Check out [DDP documentation](device.md) for more details
+- `@register_logger()` registers your class into our factory, allowing it to be instantiated from a string.
+- `sg_logger_params` only requires `project_name`, the rest is provided by the Trainer.
 
 
 ## Uploading custom objects with a callback