Comments (3)
Swin transformer uses non overlapping attention windows with local attention, which is different from this model, this was done to combat the quadratic complexity of increasing the patch numbers resulting from smaller patches.
Using flash attention, this model can directly take the 1024 pixel input which somewhat addresses that issue (patch size is stuck at 14 but allows for higher resolution images).
Now if you want to have local attention within a bigger image, nothing stops you from cropping your image in 4, 9,16... non overlapping pieces and then feeding these into the network. This would result in local attention within these pieces.
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Hi,
As noted by @ccharest93 , the model architecture is simply different, you won't get the same feature map shapes as in a Swin.
If you'd like different resolutions of feature maps (eg to input to a decoder such as upernet), you can downsample high-res feature maps with avg pooling (the general idea in https://arxiv.org/abs/2203.16527)
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Related:
- #2 (comment)
get_intermediate_layers()
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Related Issues (20)
- Regarding the emergence property HOT 1
- Strange collapse issue with the dino loss HOT 5
- Rotation invariant features HOT 1
- Loading Pretrained Models(dinov2_vits14_pretrain.pth, inov2_vitb14/dinov2_vitb14_pretrain.pth) HOT 2
- The PCA visualization HOT 3
- Has anyone successfully replaced the backbone of Mask2Fromer with dinov2 for both training and inference? HOT 3
- Can we have a model with different embedding size if we finetune on our data? HOT 3
- What's the purpose for adding Identity() in chunked blocks? HOT 1
- why 'assert self.in_channels == self.channels' in 'dinov2/eval/segmentation/models/decode_heads/linear_head.py '
- On the question of knowledge distillation HOT 8
- Can I use dino model to match images with pixels? Just like with the clip model you can match pixels with text.
- Can overfitting lead to high-norm patches? HOT 10
- "inverted" first principal component of vitb14_reg4 HOT 1
- Intermediate checkpoints outperforming final checkpoint? HOT 3
- Issuess of unexpected keyword argument 'antialias' when using the DINOv2 backbone for finetuing HOT 1
- About the inability to successfully train Dinov 2-Distill
- The loss value about training is significant HOT 3
- Requirements are uncompatible HOT 8
- Choice of Segmentation Head HOT 5
- Training mask2former head with ViT adapter for semantic segmentation HOT 1
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