alinlab / selfpatch Goto Github PK

Hello! Very interesting work.

I would like to try it out using a patch size 8. Would you be able to add support to it as it not currently compatible?

Thanks!

Hi,
Would you able to upload any of your trained model ?

Hello, I would appreciate it if you could respond to some of my questions below:

1. Could you clarify why SelfPatch loss is only evaluated when v == iq (student and teacher operate on the same view)?
2. Why is teacher output viewed locally as well? According to what I understand of the original DINO, only 2 global views pass through the teacher
3. Why is loc=False given to student_output?

student_output = [student(torch.cat(images[:2]), head_only=True, loc=False), student(torch.cat(images[2:]), head_only=True, loc=False)]

Thanks for your time and kindness!

Hi, thanks for your excellent work!

I would like to know that if there are any ground truths involved for the DINO-SelfPatch fine-tuning with Mask R-CNN/FPN ?

Since I am collecting so-called fully self-supervised segmentation models where no any labels are used, I am wondering that if you simply followed MM-Detection and thus using ground truth for the fine-tuning of pertained model to adapt to COCO detection/segmentation tasks ?

Thanks !

Dear Authors,

I hope this message finds you well. I wanted to express my appreciation for your excellent work and express my interest in using your pretrained models for my own research.

Unfortunately, I have not been able to locate any pretrained models with a patch size of 8 x 8. Would it be possible for you to provide me with these models or suggest any alternative solutions?

Thank you very much for your time and consideration. I look forward to your response.

Best regards, Xin

Thanks for your exciting work!!
The provided pre-training command seems for training 300epochs on Imagenet. I wonder whether the provided checkpoint file 'dino_selfpatch.pth' is pre-trained with 200epochs or 300epochs. As mentioned in the paper, you pre-train selfpatch in imagenet for 200epochs. And if I want to reproduce your results, shouldI add '--epochs 200' in the provided pre-training command?

From how i knew, In Dino loss Teacher Model uses only global views. and Student Model uses both global and local views.
However in your code. Teacher Model uses both global and local views like student model.

Did you intentionally add local views in teacher model?
If so how is this same as original Dino loss?

Input code :
teacher_output = [teacher(torch.cat(images[:2]), head_only=True, loc=True), teacher(torch.cat(images[2:]), head_only=True, loc=True)]
loss code :
teacher_cls = teacher_output[0][0].chunk(2) + teacher_output[1][0].chunk(self.ncrops-2)

What does loc_weight represent?