Open stephanecharette opened 4 years ago
When setting up new projects, should I default to YOLOv4 instead of v3?
Yes.
Worded differently: are there reasons why I would use v3 instead of v4?
Only if you want to use other frameworks (TF, Pytorch, ...) which don't support YOLOv4 yet.
Lastly, I see 2 v4 .cfg files. The only real difference between them is learning_rate=0.001 and learning_rate=0.00261. How do we choose which one to use?
Since yolov4.cfg may be unstable for some other datasets, we provide yolov4-custom.cfg with slightly lower learninig_rate. I think we will fix this issue by using max_delta=5
in the [yolo]
layers instead of decreasingf learninig_rate.
Thanks, Alexey. I see that YOLOv4 has 162 layers. Compared to 107 in YOLOv3 and 24 layers in YOLOv3-Tiny. Especially when going from v3-Tiny, that is quite the jump. I haven't tried to retrain any of my existing networks in v4 yet, but I suspect that processing time per frame will increase significantly when going from v3-Tiny to v4. Several of my neural networks run on small devices, like Jetson Nano, which have limited computing power.
Are there plans on defining a v4-Tiny configuration?
Are there plans on defining a v4-Tiny configuration?
Yes.
Looking forward to your v4-tiny configuration.
So when is the exciting time?
+1! Waiting for v4-tiny, thank you @AlexeyAB
YOLOv4-tiny released: 40.2% AP50, 371 FPS (GTX 1080 Ti)
I see there is now a YOLOv4! This is exciting. Can someone comment on some of the highlights/differences between v3 and v4? The readme doesn't have much details.
When setting up new projects, should I default to YOLOv4 instead of v3? Worded differently: are there reasons why I would use v3 instead of v4?
Most of the darknet projects I've done in the past have been YOLOv3-tiny. But it looks like there is no such thing when it comes to v4. Is this in the works?
Lastly, I see 2 v4 .cfg files. The only real difference between them is
learning_rate=0.001
andlearning_rate=0.00261
. How do we choose which one to use?