ultralytics 8.2.2 replace COCO128 with COCO8 (#10167)

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

docs/en/tasks/detect.md

@@ -42,11 +42,11 @@ YOLOv8 pretrained Detect models are shown here. Detect, Segment and Pose models
 | [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) | 640                   | 53.9                 | 479.1                          | 3.53                                | 68.2               | 257.8             |
 
 - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0`
-- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0|cpu`
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=coco8.yaml batch=1 device=0|cpu`
 
 ## Train
 
-Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
+Train YOLOv8n on the COCO8 dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
 
 !!! Example
 
@@ -61,19 +61,19 @@ Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a ful
         model = YOLO('yolov8n.yaml').load('yolov8n.pt')  # build from YAML and transfer weights
 
         # Train the model
-        results = model.train(data='coco128.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
         ```
     === "CLI"
 
         ```bash
         # Build a new model from YAML and start training from scratch
-        yolo detect train data=coco128.yaml model=yolov8n.yaml epochs=100 imgsz=640
+        yolo detect train data=coco8.yaml model=yolov8n.yaml epochs=100 imgsz=640
 
         # Start training from a pretrained *.pt model
-        yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640
 
         # Build a new model from YAML, transfer pretrained weights to it and start training
-        yolo detect train data=coco128.yaml model=yolov8n.yaml pretrained=yolov8n.pt epochs=100 imgsz=640
+        yolo detect train data=coco8.yaml model=yolov8n.yaml pretrained=yolov8n.pt epochs=100 imgsz=640
         ```
 
 ### Dataset format
@@ -82,7 +82,7 @@ YOLO detection dataset format can be found in detail in the [Dataset Guide](../d
 
 ## Val
 
-Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's training `data` and arguments as model attributes.
+Validate trained YOLOv8n model accuracy on the COCO8 dataset. No argument need to passed as the `model` retains it's training `data` and arguments as model attributes.
 
 !!! Example
 

docs/en/tasks/segment.md

@@ -42,7 +42,7 @@ YOLOv8 pretrained Segment models are shown here. Detect, Segment and Pose models
 | [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-seg.pt) | 640                   | 53.4                 | 43.4                  | 712.1                          | 4.02                                | 71.8               | 344.1             |
 
 - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org) dataset. <br>Reproduce by `yolo val segment data=coco.yaml device=0`
-- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu`
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco8-seg.yaml batch=1 device=0|cpu`
 
 ## Train
 
@@ -61,19 +61,19 @@ Train YOLOv8n-seg on the COCO128-seg dataset for 100 epochs at image size 640. F
         model = YOLO('yolov8n-seg.yaml').load('yolov8n.pt')  # build from YAML and transfer weights
 
         # Train the model
-        results = model.train(data='coco128-seg.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco8-seg.yaml', epochs=100, imgsz=640)
         ```
     === "CLI"
 
         ```bash
         # Build a new model from YAML and start training from scratch
-        yolo segment train data=coco128-seg.yaml model=yolov8n-seg.yaml epochs=100 imgsz=640
+        yolo segment train data=coco8-seg.yaml model=yolov8n-seg.yaml epochs=100 imgsz=640
 
         # Start training from a pretrained *.pt model
-        yolo segment train data=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=coco8-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
 
         # Build a new model from YAML, transfer pretrained weights to it and start training
-        yolo segment train data=coco128-seg.yaml model=yolov8n-seg.yaml pretrained=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=coco8-seg.yaml model=yolov8n-seg.yaml pretrained=yolov8n-seg.pt epochs=100 imgsz=640
         ```
 
 ### Dataset format