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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

utils/loggers/comet/README.md

@@ -102,12 +102,12 @@ Logging Models to Comet is disabled by default. To enable it, pass the `save-per
 
 ```shell
 python train.py \
---img 640 \
---batch 16 \
---epochs 5 \
---data coco128.yaml \
---weights yolov5s.pt \
---save-period 1
+  --img 640 \
+  --batch 16 \
+  --epochs 5 \
+  --data coco128.yaml \
+  --weights yolov5s.pt \
+  --save-period 1
 ```
 
 ## Logging Model Predictions
@@ -122,12 +122,12 @@ Here is an [example project using the Panel](https://www.comet.com/examples/come
 
 ```shell
 python train.py \
---img 640 \
---batch 16 \
---epochs 5 \
---data coco128.yaml \
---weights yolov5s.pt \
---bbox_interval 2
+  --img 640 \
+  --batch 16 \
+  --epochs 5 \
+  --data coco128.yaml \
+  --weights yolov5s.pt \
+  --bbox_interval 2
 ```
 
 ### Controlling the number of Prediction Images logged to Comet
@@ -136,12 +136,12 @@ When logging predictions from YOLOv5, Comet will log the images associated with
 
 ```shell
 env COMET_MAX_IMAGE_UPLOADS=200 python train.py \
---img 640 \
---batch 16 \
---epochs 5 \
---data coco128.yaml \
---weights yolov5s.pt \
---bbox_interval 1
+  --img 640 \
+  --batch 16 \
+  --epochs 5 \
+  --data coco128.yaml \
+  --weights yolov5s.pt \
+  --bbox_interval 1
 ```
 
 ### Logging Class Level Metrics
@@ -150,11 +150,11 @@ Use the `COMET_LOG_PER_CLASS_METRICS` environment variable to log mAP, precision
 
 ```shell
 env COMET_LOG_PER_CLASS_METRICS=true python train.py \
---img 640 \
---batch 16 \
---epochs 5 \
---data coco128.yaml \
---weights yolov5s.pt
+  --img 640 \
+  --batch 16 \
+  --epochs 5 \
+  --data coco128.yaml \
+  --weights yolov5s.pt
 ```
 
 ## Uploading a Dataset to Comet Artifacts
@@ -165,12 +165,12 @@ The dataset be organized in the way described in the [YOLOv5 documentation](http
 
 ```shell
 python train.py \
---img 640 \
---batch 16 \
---epochs 5 \
---data coco128.yaml \
---weights yolov5s.pt \
---upload_dataset
+  --img 640 \
+  --batch 16 \
+  --epochs 5 \
+  --data coco128.yaml \
+  --weights yolov5s.pt \
+  --upload_dataset
 ```
 
 You can find the uploaded dataset in the Artifacts tab in your Comet Workspace <img width="1073" alt="artifact-1" src="https://user-images.githubusercontent.com/7529846/186929193-162718bf-ec7b-4eb9-8c3b-86b3763ef8ea.png">
@@ -192,11 +192,11 @@ Then pass this file to your training script in the following way
 
 ```shell
 python train.py \
---img 640 \
---batch 16 \
---epochs 5 \
---data artifact.yaml \
---weights yolov5s.pt
+  --img 640 \
+  --batch 16 \
+  --epochs 5 \
+  --data artifact.yaml \
+  --weights yolov5s.pt
 ```
 
 Artifacts also allow you to track the lineage of data as it flows through your Experimentation workflow. Here you can see a graph that shows you all the experiments that have used your uploaded dataset. <img width="1391" alt="artifact-4" src="https://user-images.githubusercontent.com/7529846/186929264-4c4014fa-fe51-4f3c-a5c5-f6d24649b1b4.png">
@@ -211,7 +211,7 @@ This will restore the run to its state before the interruption, which includes r
 
 ```shell
 python train.py \
---resume "comet://<your run path>"
+  --resume "comet://<your run path>"
 ```
 
 ## Hyperparameter Search with the Comet Optimizer