Add Dockerfiles and update Docs README (#124)

Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

README.md

@@ -5,7 +5,9 @@
 ```bash
 pip install ultralytics
 ```
+
 Development
+
 ```
 git clone https://github.com/ultralytics/ultralytics
 cd ultralytics
@@ -13,25 +15,34 @@ pip install -e .
 ```
 
 ## Usage
+
 ### 1. CLI
+
 To simply use the latest Ultralytics YOLO models
+
 ```bash
 yolo task=detect    mode=train    model=yolov8n.yaml      args=...
           classify       predict        yolov8n-cls.yaml  args=...
           segment        val            yolov8n-seg.yaml  args=...
                          export         yolov8n.pt        format=onnx
 ```
+
 ### 2. Python SDK
+
 To use pythonic interface of Ultralytics YOLO model
+
 ```python
 from ultralytics import YOLO
 
-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)
+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.train(data="coco128.yaml", epochs=100, imgsz=640, ...)
 results = model.val()
-results = model.predict(source='bus.jpg')
-success = model.export(format='onnx')
+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.
+
+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.