ultralytics 8.2.2 replace COCO128 with COCO8 (#10167)

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

examples/YOLOv8-ONNXRuntime/main.py

@@ -30,7 +30,7 @@ class YOLOv8:
         self.iou_thres = iou_thres
 
         # Load the class names from the COCO dataset
-        self.classes = yaml_load(check_yaml("coco128.yaml"))["names"]
+        self.classes = yaml_load(check_yaml("coco8.yaml"))["names"]
 
         # Generate a color palette for the classes
         self.color_palette = np.random.uniform(0, 255, size=(len(self.classes), 3))

examples/YOLOv8-OpenCV-ONNX-Python/main.py

@@ -8,7 +8,7 @@ import numpy as np
 from ultralytics.utils import ASSETS, yaml_load
 from ultralytics.utils.checks import check_yaml
 
-CLASSES = yaml_load(check_yaml("coco128.yaml"))["names"]
+CLASSES = yaml_load(check_yaml("coco8.yaml"))["names"]
 colors = np.random.uniform(0, 255, size=(len(CLASSES), 3))
 
 

examples/YOLOv8-OpenCV-int8-tflite-Python/main.py

@@ -102,7 +102,7 @@ class Yolov8TFLite:
         self.iou_thres = iou_thres
 
         # Load the class names from the COCO dataset
-        self.classes = yaml_load(check_yaml("coco128.yaml"))["names"]
+        self.classes = yaml_load(check_yaml("coco8.yaml"))["names"]
 
         # Generate a color palette for the classes
         self.color_palette = np.random.uniform(0, 255, size=(len(self.classes), 3))

examples/YOLOv8-Segmentation-ONNXRuntime-Python/main.py

@@ -37,7 +37,7 @@ class YOLOv8Seg:
         self.model_height, self.model_width = [x.shape for x in self.session.get_inputs()][0][-2:]
 
         # Load COCO class names
-        self.classes = yaml_load(check_yaml("coco128.yaml"))["names"]
+        self.classes = yaml_load(check_yaml("coco8.yaml"))["names"]
 
         # Create color palette
         self.color_palette = Colors()

examples/tutorial.ipynb

@@ -425,7 +425,7 @@
         "model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)\n",
         "\n",
         "# Use the model\n",
-        "results = model.train(data='coco128.yaml', epochs=3)  # train the model\n",
+        "results = model.train(data='coco8.yaml', epochs=3)  # train the model\n",
         "results = model.val()  # evaluate model performance on the validation set\n",
         "results = model('https://ultralytics.com/images/bus.jpg')  # predict on an image\n",
         "results = model.export(format='onnx')  # export the model to ONNX format"
@@ -467,7 +467,7 @@
         "from ultralytics import YOLO\n",
         "\n",
         "model = YOLO('yolov8n.pt')  # load a pretrained YOLOv8n detection model\n",
-        "model.train(data='coco128.yaml', epochs=3)  # train the model\n",
+        "model.train(data='coco8.yaml', epochs=3)  # train the model\n",
         "model('https://ultralytics.com/images/bus.jpg')  # predict on an image"
       ],
       "metadata": {
@@ -494,7 +494,7 @@
         "from ultralytics import YOLO\n",
         "\n",
         "model = YOLO('yolov8n-seg.pt')  # load a pretrained YOLOv8n segmentation model\n",
-        "model.train(data='coco128-seg.yaml', epochs=3)  # train the model\n",
+        "model.train(data='coco8-seg.yaml', epochs=3)  # train the model\n",
         "model('https://ultralytics.com/images/bus.jpg')  # predict on an image"
       ],
       "metadata": {