Comments (6)
@nooralahzadeh @lucidrains I'm hoping I have a chance to tackle this by early next week. A quick look at their model suggests it should be fairly quick to port their weights to either this one or my PyTorch impl. It should be a matter of reshaping and possibly concating some weights or conversely, splitting some linear layers.
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@lucidrains and likewise thank you re the attention work. Now that latest attention and transformer ideas are colliding with CV in a big way, it's great to have reliably correct, clean PyTorch impl of the latest ideas to compare against or borrow from :)
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@rwightman thank you for all your work in the CV space :D
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@nooralahzadeh hmmm, I'm almost sure either @rwightman would do it, or @huggingface will make a grand entrance into the computer vision space. either way, I'll do it if they don't do it within a week or two :)
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@nooralahzadeh released by Ross! https://github.com/rwightman/pytorch-image-models
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Did you find the pre-trained model?
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Related Issues (20)
- structural 3D ViT HOT 4
- Saving and loading model seems to be regressing to lower performance HOT 1
- vit_pytorch -> cross_vit.py(mistake) HOT 1
- Using vision transformers for different image resolutions HOT 1
- Potential regression with PT 2.0 and CUDA 12.2/CuDNN 8.9.4 HOT 1
- Not correctly understanding the Multi Head Attention part of the ViT implementation... HOT 3
- CvT with 1 channel input data HOT 2
- Layernorm in Cross attention HOT 4
- how to train HOT 2
- Questions about distill_loss HOT 1
- Trouble loading ViT - Dino structure for channels>3?
- Question regarding 1d fft use HOT 1
- Masking attention with batches
- can we use CvT model for segmentation?
- Multi-target Regression Question
- Problems regarding training 3D Vision transformer : model does not converge
- Add implementation of LongVit HOT 4
- A question with ViT 3d
- Cuda memory for 3D VIT HOT 2
- PyPi page markdown render HOT 1
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