This is why I have so much doubt. To claim it's better in any meaningful way you need to show it on the same framework, varied datasets, varied input sizes and you should be able to use it in your detection problem and also see some benefits from the previous version.
> SIZE: YOLOv5 is about 88% smaller than YOLOv4 (27 MB vs 244 MB)
Is that a benefit of Darknet vs TF, YOLOv4 vs YOLOv5, or did you win the NN lottery [1]?
> SPEED: YOLOv5 is about 180% faster than YOLOv4 (140 FPS vs 50 FPS)
Again, where does this improvement come from?
> ACCURACY: YOLOv5 is roughly as accurate as YOLOv4 on the same task (0.895 mAP vs 0.892 mAP)
The difference in 0.1% accuracy can be huge, for example the difference between 99.9% and 100% could require an insanely larger neural network. Even much less that 99% accuracy, it seems clear to me that there can still be some limitations on accuracy from neural network size.
For example, if you really don't care so much for accuracy, you can really squeeze the network down [2].
> SIZE: YOLOv5 is about 88% smaller than YOLOv4 (27 MB vs 244 MB)
Is that a benefit of Darknet vs TF, YOLOv4 vs YOLOv5, or did you win the NN lottery [1]?
> SPEED: YOLOv5 is about 180% faster than YOLOv4 (140 FPS vs 50 FPS)
Again, where does this improvement come from?
> ACCURACY: YOLOv5 is roughly as accurate as YOLOv4 on the same task (0.895 mAP vs 0.892 mAP)
The difference in 0.1% accuracy can be huge, for example the difference between 99.9% and 100% could require an insanely larger neural network. Even much less that 99% accuracy, it seems clear to me that there can still be some limitations on accuracy from neural network size.
For example, if you really don't care so much for accuracy, you can really squeeze the network down [2].
[1] https://ai.facebook.com/blog/understanding-the-generalizatio...
[2] https://arxiv.org/abs/1910.03159