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
Hi, thanks for your work. I'm currently following your work. However, I can not reimplement the result in the paper. My reimplementation is just 70.95. The followings are my reimplementation:
I use the this repo to generate the scribble masks.
I found the resnet101 model in current repo is different from the original one. In original resnet101, the conv1 is a single convolutional layer with kernel size 7*7, stride 2 and output channel 64. In current repo, the conv1 is a "Conv block" with conv(3,64,3,2,1)-bn-relu-conv(64,64,3,1,1)-bn-relu-conv(64,128,3,1,1). As I found no pretrained model released, I replace the current one to the pytorch offical res101 model, and load the pretrained model from pytorch model zone.
The text was updated successfully, but these errors were encountered:
Thanks for your kind comments.
The pre-trained models and weak labels are released now. Specifically, we use the weak labels provided by TEL.
Feel free to contact me if you have any questions.
Hi, thanks for your work. I'm currently following your work. However, I can not reimplement the result in the paper. My reimplementation is just 70.95. The followings are my reimplementation:
conv1
is a single convolutional layer with kernel size 7*7, stride 2 and output channel 64. In current repo, theconv1
is a "Conv block" withconv(3,64,3,2,1)-bn-relu-conv(64,64,3,1,1)-bn-relu-conv(64,128,3,1,1)
. As I found no pretrained model released, I replace the current one to the pytorch offical res101 model, and load the pretrained model from pytorch model zone.The text was updated successfully, but these errors were encountered: