ultralytics 8.2.2 replace COCO128 with COCO8 (#10167)

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

docs/en/integrations/ray-tune.md

@@ -112,13 +112,13 @@ In this example, we demonstrate how to use a custom search space for hyperparame
     model = YOLO("yolov8n.pt")
 
     # Run Ray Tune on the model
-    result_grid = model.tune(data="coco128.yaml",
+    result_grid = model.tune(data="coco8.yaml",
                              space={"lr0": tune.uniform(1e-5, 1e-1)},
                              epochs=50,
                              use_ray=True)
     ```
 
-In the code snippet above, we create a YOLO model with the "yolov8n.pt" pretrained weights. Then, we call the `tune()` method, specifying the dataset configuration with "coco128.yaml". We provide a custom search space for the initial learning rate `lr0` using a dictionary with the key "lr0" and the value `tune.uniform(1e-5, 1e-1)`. Finally, we pass additional training arguments, such as the number of epochs directly to the tune method as `epochs=50`.
+In the code snippet above, we create a YOLO model with the "yolov8n.pt" pretrained weights. Then, we call the `tune()` method, specifying the dataset configuration with "coco8.yaml". We provide a custom search space for the initial learning rate `lr0` using a dictionary with the key "lr0" and the value `tune.uniform(1e-5, 1e-1)`. Finally, we pass additional training arguments, such as the number of epochs directly to the tune method as `epochs=50`.
 
 ## Processing Ray Tune Results