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README.md

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 # Self-supervised ViT-S/16 (small-sized Vision Transformer with patch size 16) model with SimPool
 
 ViT-S model with SimPool (gamma=1.25) trained on ImageNet-1k for 100 epochs. Self-supervision with DINO.
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 SimPool is a simple attention-based pooling method at the end of network, introduced on this ICCV 2023 [paper](https://arxiv.org/pdf/2309.06891.pdf) and released in this [repository](https://github.com/billpsomas/simpool/).
 Disclaimer: This model card is written by the author of SimPool, i.e. [Bill Psomas](http://users.ntua.gr/psomasbill/).
 
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 Interestingly, we find that, whether supervised or self-supervised, SimPool improves performance on pre-training and downstream tasks and provides attention maps delineating object boundaries in all cases.
 One could thus call SimPool universal.
 
+> :loudspeaker: **NOTE: Considering integrating SimPool into your workflow?**  
+> Use SimPool when you need attention maps of the highest quality, delineating object boundaries.
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 ## BibTeX entry and citation info
 
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