Make YOLO a module (#111)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

README.md

@@ -16,19 +16,21 @@ pip install -e .
 ### 1. CLI
 To simply use the latest Ultralytics YOLO models
 ```bash
-yolo task=detect    mode=train  model=s.yaml ...
-          classify       infer        s-cls.yaml
-          segment        val          s-seg.yaml
+yolo task=detect    mode=train     model=yolov8n.yaml ...
+          classify       predict         yolov8n-cls.yaml
+          segment        val             yolov8n-seg.yaml
 ```
 ### 2. Python SDK
 To use pythonic interface of Ultralytics YOLO model
 ```python
-import ultralytics
 from ultralytics import YOLO
 
-model = YOLO()
-model.new("s-seg.yaml") # automatically detects task type
-model.load("s-seg.pt") # load checkpoint
-model.train(data="coco128-segments", epochs=1, lr0=0.01, ...)
+model = YOLO.new('yolov8n.yaml')  # create a new model from scratch
+model = YOLO.load('yolov8n.pt')  # load a pretrained model (recommended for best training results)
+
+results = model.train(data='coco128.yaml', epochs=100, imgsz=640, ...)
+results = model.val()
+results = model.predict(source='bus.jpg')
+success = model.export(format='onnx')
 ```
 If you're looking to modify YOLO for R&D or to build on top of it, refer to [Using Trainer]() Guide on our docs.