You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I'm trying to run this on low-resolution images that contain one human. The images are about ~ 50x100 pix, so much smaller than the model boxsize. The program doesn't work very well on such low-resolution images. The majority of them do not have joints detected well. Though heatmaps appear to be ok.
I was wondering if someone would have any tips on how to modify the program to perform better on low res images. Thanks.
Here's example of an image that didn't work.
The text was updated successfully, but these errors were encountered:
Algorithm operates on cells 8x8 pixels size. So this image will be only 6x12 cells, probably you could retrain it with 4x4 or 2x2 cells for low res images.
Thanks @anatolix , could you elaborate a bit on cells? And how you imagine changing the size of the cells?
As I understand the model, the dimension reduction only happens in VGG16 block of the model. Three pooling layers in VGG reduce the dimension by 8, so eventual heatmaps and PAFs are 1/8th of the original size. Since VGG is so essential for the model, I struggle to see how to reduce the cell size. Would you remove the pooling layers from VGG? That seems like it could do more harm.
I'm trying to run this on low-resolution images that contain one human. The images are about ~ 50x100 pix, so much smaller than the model
boxsize
. The program doesn't work very well on such low-resolution images. The majority of them do not have joints detected well. Though heatmaps appear to be ok.I was wondering if someone would have any tips on how to modify the program to perform better on low res images. Thanks.
Here's example of an image that didn't work.
The text was updated successfully, but these errors were encountered: