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[TPAMI 2022 & CVPR2021 Oral] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

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backbone的build时会构建position embeding,需要用到nestedtensor的mask,请问输入tensor类型的不会报错吗,我看代码里没有做相应的分支处理 ![1659264187(1)](https://user-images.githubusercontent.com/31236750/182022485-9ff0aea4-4e6a-4d0b-95c6-58d517ae087d.jpg)

Hello there! As the code for the one-shot object detection task is not available in this repository, would there be any way to access it? If not would it be...

Hi authors, This is not about an issue :smile: UP-DETR is great! I just wrote a blog about it, see here: https://medium.com/analytics-vidhya/up-detr-unsupervised-pre-training-for-object-detection-with-transformers-paper-explained-84611e27a144 You could consider to put its link in...

Hi, Thanks your works. I use my dataset pretrial on the you provided imagenet model. Then I use my pretreaind model to finetune on my dataset. The code return _IncompatibleKeys(missing_keys=['class_embed.weight',...

excuse me, I Fine-tuning in own dataset and evaluation This is mine warning after evaluation in pycharm(win 10),pytorch==1.12.1,torchvision==0.13.1,cuda==11.7,3070ti Unexpected key(s) in state_dict: "feature_align.layers.0.weight", "feature_align.layers.0.bias", "feature_align.layers.1.weight", "feature_align.layers.1.bias", "patch2query.weight", "patch2query.bias". I dont...

I have the opposite situation. RuntimeError: Error(s) in loading state_dict for UPDETR: size mismatch for class_embed.weight: copying a param with shape torch.Size([3, 256]) from checkpoint, the shape in current model...