multistage loss

[soans1994]

hello author,

i have some queries regarding your work. I have worked recently with multistage model. for example, I used 5 stages, 5 outputs and 5 individual losses. (So, i had to use generate 5 similar labels to compute loss.),(i think even if i dont return multiple labels, the loss is computed from the single lable returned from the dataloader.)

for stacked hourglass, should i use similar method?
edit: im training your hourglass model with mse loss, how many stages are better? also how can i choose features 128 or 256? whats the difference? more number of filters?

thank you

[david8862]

@soans1994

1. Yes, and currently the data generator has provided multi labels as a list for train.
2. Original paper use stack=8 and features=256, but generally stack=2 has been enough to get a reasonable result

[soans1994]

thank you,

1. Is it okay if i dont return list, but a single label?
2. okay i will try these values.

I have updampled the stage output with 4x4 kernel to get 96x96 output since my input image is very less 96x96 image. Is it okay if i upscale the stage outputs to visualise while inferencing.

[david8862]

thank you,

1. Is it okay if i dont return list, but a single label?

should be no since keras data generator doesn't support auto duplicate label in loss

2. okay i will try these values.

I have updampled the stage output with 4x4 kernel to get 96x96 output since my input image is very less 96x96 image. Is it okay if i upscale the stage outputs to visualise while inferencing.

I've not tried that and not sure about the model performance