🚀 Feature

It's time to streamline finetuning in YOLOv5. It's become evident that finetuning and training from scratch likely require different set of hyps to each perform their best. I see two action items for this:

1. Remove hardcoded hyps from train.py into a seperate coco.hyp.yaml. This sets the stage for defining a second coco.finetune.hyp.yaml with new hyps defined via hyp evolution on a finetune scenario (probably VOC 50 epochs).

2. Update the train configuration to allow single-file start, vs today's dual-file requirements (i.e. a pair of yolov5s.yaml and yolov5s.pt). This change is now available as the v2.0 format embeds a model's original yaml as a class property:
   

   yolov5/models/yolo.py

   Lines 64 to 67 in d7cfbc4

   	self.yaml_file = Path(cfg).name
   	with open(cfg) as f:
   	self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict

This should allow us to better define default training settings depending on whether finetune mode is used or not. finetune model would automatically be set to true if a *.pt model argument was supplied to train.py instead of a *.yaml argument. Drawback is that all the tutorials will need updating :(