Dear Mohit Jain,
Thanks for your advice, My net is something like R-CNN, which is finetuned from CaffeNet, so I think dropout is in consideration:
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 10
decay_mult: 1
}
My data is random selected in windowdatalayer, but in input txt, I just used data in index(I think it's OK because windowdatalayer will random prefetch inputs).
Input txt is image path and box position which in windowdatalayer will resize to image patches and will be random selected. The real inputs in net is image patches
from box.
I don't clearly understand how to perform multiple rounds in this problem. Should I just force set equal samples for each category for training?
在 2015年12月13日星期日 UTC+8下午6:14:59,Mohit Jain写道: