Update ultralytics/yolov5 Transforms

1. update ultralytics/yolov5(485da42) transforms, add Mosaic/Perspective and so on.

• Train using the VOC07+12 trainval dataset and test using the VOC2007 Test dataset with an input size of 416x416. give the result as follows

	Original (darknet)	tztztztztz/yolov2.pytorch	zjykzj/YOLOv2(This)	zjykzj/YOLOv2(This)	zjykzj/YOLOv2(This)
ARCH	YOLOv2	YOLOv2	YOLOv2+Darknet53	YOLOv2	YOLOv2-tiny
VOC AP[IoU=0.50]	76.8	72.7	74.95/76.33(v0.2.1)	73.27	65.44

• Train using the COCO train2017 dataset and test using the COCO val2017 dataset with an input size of 416x416. give the result as follows (Note: The results of the original paper were evaluated on the COCO test-dev2015 dataset)

	Original (darknet)	zjykzj/YOLOv2(This)	zjykzj/YOLOv2(This)
ARCH	YOLOv2	YOLOv2+Darknet53	YOLOv2
COCO AP[IoU=0.50:0.95]	21.6	25.86	22.84
COCO AP[IoU=0.50]	44.0	48.40	43.95

From the training results, it can be seen that the pretraining configuration of yolov5 can effectively improve the performance of yolov2. However, it can also be observed that the improvement is not very high, and the performance of YOLOv2+Darknet53 is not as good as the previous results. This indicates that there are different optimal training configurations for different network architectures.