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  2. It is also good to mention that YOLOv5 was released with five different sizes: n for extra small (nano) size model. s for small size model. m for medium size model. l for large size model x for extra large size model
    UPDATE: The YOLOv5 model tests is YOLOv5s, which is 27MB. The YOLOv4 model tested is "big YOLOv4," which is 250 MB. The biggest YOLOv5 implementation, YOLOv5l, is 192 MB.
    blog.roboflow.com/yolov5-is-here/
    YOLOv5 is smaller and generally easier to use in production. Given it is natively implemented in PyTorch (rather than Darknet), modifying the architecture and exporting to many deploy environments is straightforward. SIZE: YOLOv5s is about 88% smaller than big-YOLOv4 (27 MB vs 244 MB)
     
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