ultralytics 8.0.55 unified YOLOv8 model YAMLs (ultralytics#1475)

Author: glenn-jocher

.github/workflows/greetings.yml

@@ -52,4 +52,4 @@ jobs:
 
             <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml?query=event%3Aschedule"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
 
-            If this badge is green, all [Ultralytics CI](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml?query=event%3Aschedule) tests are currently passing. CI tests verify correct operation of all YOLOv8 modes and tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
+            If this badge is green, all [Ultralytics CI](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml?query=event%3Aschedule) tests are currently passing. CI tests verify correct operation of all YOLOv8 [Modes](https://docs.ultralytics.com/modes/) and [Tasks](https://docs.ultralytics.com/tasks/) on macOS, Windows, and Ubuntu every 24 hours and on every commit.

docs/tasks/classify.md

@@ -26,16 +26,25 @@ see the [Configuration](../usage/cfg.md) page.
         from ultralytics import YOLO
         
         # Load a model
-        model = YOLO('yolov8n-cls.yaml')  # build a new model from scratch
+        model = YOLO('yolov8n-cls.yaml')  # build a new model from YAML
         model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+        model = YOLO('yolov8n-cls.yaml').load('yolov8n-cls.pt')  # build from YAML and transfer weights
         
         # Train the model
         model.train(data='mnist160', epochs=100, imgsz=64)
         ```
+
     === "CLI"
-    
+
         ```bash
+        # Build a new model from YAML and start training from scratch
+        yolo classify train data=mnist160 model=yolov8n-cls.yaml epochs=100 imgsz=64
+
+        # Start training from a pretrained *.pt model
         yolo classify train data=mnist160 model=yolov8n-cls.pt epochs=100 imgsz=64
+
+        # Build a new model from YAML, transfer pretrained weights to it and start training
+        yolo classify train data=mnist160 model=yolov8n-cls.yaml pretrained=yolov8n-cls.pt epochs=100 imgsz=64
         ```
 
 ## Val

docs/tasks/detect.md

@@ -26,16 +26,24 @@ the [Configuration](../usage/cfg.md) page.
         from ultralytics import YOLO
         
         # Load a model
-        model = YOLO('yolov8n.yaml')  # build a new model from scratch
+        model = YOLO('yolov8n.yaml')  # build a new model from YAML
         model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO('yolov8n.yaml').load('yolov8n.pt')  # build from YAML and transfer weights
         
         # Train the model
         model.train(data='coco128.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
+
+        # Start training from a pretrained *.pt model
         yolo detect train data=coco128.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
         ```
 
 ## Val

docs/tasks/keypoints.md

@@ -28,16 +28,24 @@ train an OpenPose model on a custom dataset, see the OpenPose Training page.
         from ultralytics import YOLO
         
         # Load a model
-        model = YOLO('yolov8n.yaml')  # build a new model from scratch
+        model = YOLO('yolov8n.yaml')  # build a new model from YAML
         model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO('yolov8n.yaml').load('yolov8n.pt')  # build from YAML and transfer weights
         
         # Train the model
         model.train(data='coco128.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
+
+        # Start training from a pretrained *.pt model
         yolo detect train data=coco128.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
         ```
 
 ## Val TODO

docs/tasks/segment.md

@@ -26,16 +26,24 @@ arguments see the [Configuration](../usage/cfg.md) page.
         from ultralytics import YOLO
         
         # Load a model
-        model = YOLO('yolov8n-seg.yaml')  # build a new model from scratch
+        model = YOLO('yolov8n-seg.yaml')  # build a new model from YAML
         model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
+        model = YOLO('yolov8n-seg.yaml').load('yolov8n.pt')  # build from YAML and transfer weights
         
         # Train the model
         model.train(data='coco128-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
+
+        # Start training from a pretrained *.pt model
         yolo segment train data=coco128-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
         ```
 
 ## Val

docs/usage/cfg.md

@@ -143,7 +143,7 @@ given task.
 | `agnostic_nms`   | `False`                | class-agnostic NMS                                       |
 | `retina_masks`   | `False`                | use high-resolution segmentation masks                   |
 | `classes`        | `None`                 | filter results by class, i.e. class=0, or class=[0,2,3]  |
-| `box`            | `True`                 | Show boxes in segmentation predictions                   |
+| `boxes`          | `True`                 | Show boxes in segmentation predictions                   |
 
 ### Validation
 

tests/test_engine.py

@@ -74,16 +74,9 @@ def test_segment():
 
 
 def test_classify():
-    overrides = {
-        'data': 'imagenet10',
-        'model': 'yolov8n-cls.yaml',
-        'imgsz': 32,
-        'epochs': 1,
-        'batch': 64,
-        'save': False}
+    overrides = {'data': 'imagenet10', 'model': 'yolov8n-cls.yaml', 'imgsz': 32, 'epochs': 1, 'save': False}
     CFG.data = 'imagenet10'
     CFG.imgsz = 32
-    CFG.batch = 64
     # YOLO(CFG_SEG).train(**overrides)  # works
 
     # Trainer

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = '8.0.54'
+__version__ = '8.0.55'
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils.checks import check_yolo as checks