dvclive: Add YOLO page.

content/docs/dvclive/ml-frameworks/index.md

@@ -13,6 +13,7 @@ We currently support the following _ML Frameworks_:
 - [PyTorch Lightning](/doc/dvclive/ml-frameworks/pytorch-lightning)
 - [TensorFlow](/doc/dvclive/ml-frameworks/tensorflow)
 - [XGBoost](/doc/dvclive/ml-frameworks/xgboost)
+- [Ultralytics YOLO v8](/doc/dvclive/ml-frameworks/yolo)
 
 The DVCLive team is happy to extend the list with more _ML Frameworks_ as
 needed. Check the

content/docs/dvclive/ml-frameworks/yolo.md

@@ -0,0 +1,25 @@
+# YOLO
+
+DVCLive allows you to add experiment tracking capabilities to your
+[Ultralytics YOLO v8](https://docs.ultralytics.com/) projects.
+
+## Usage
+
+<p align='center'>
+  <a href="https://colab.research.google.com/github/iterative/dvclive/blob/main/examples/DVCLive-YOLO.ipynb">
+    <img src="https://colab.research.google.com/assets/colab-badge.svg" />
+  </a>
+</p>
+
+If you have `dvclive` installed, the
+[DVCLive callback](https://docs.ultralytics.com/reference/utils/callbacks/dvc/)
+will be used for tracking experiments and logging `metrics`, `parameters`,
+`plots` and the best `model` automatically.
+
+```cli
+$ pip install dvclive ultralytics
+$ yolo train model=yolov8n.pt data=coco8.yaml epochs=5 imgsz=640
+```
+
+![YOLO Studio Table](/img/yolo-studio-table.png)
+![YOLO Studio Plots](/img/yolo-studio-plots.gif)

content/docs/sidebar.json

@@ -842,6 +842,10 @@
           {
             "slug": "xgboost",
             "label": "XGBoost"
+          },
+          {
+            "slug": "yolo",
+            "label": "Ultralytics YOLO v8"
           }
         ]
       },