Vision Transformer for Image Classification

Vision Transformer (ViT) is the new state-of-the-art for image classification. ViT was posted on arXiv in Oct 2020 and officially published in 2021. On all the public datasets, ViT beats the best ResNet by a small margin, provided that ViT has been pretrained on a sufficiently large dataset. The bigger the dataset, the greater the advantage of the ViT over ResNet.

Reference:
- Dosovitskiy et al. An image is worth 16×16 words: transformers for image recognition at scale. In ICLR, 2021.

[UzzalPodder]

Great Explanation with detailed notations. Most of the videos found in the YouTube were some kind of oral explanation. But this kind of symbolic notation is very helpful for garbing the real picture, specially if anyone want to re-implement or add new idea with it. Thank you so much. Please continuing helping us by making these kind of videos for us.

[mmpattnaik]

Can't stress enough on how easy to understand you made it

[drakehinst]

These are some of the best, hands-on and simple explanations I've seen in a while on a new CS method. Straight to the point with no superfluous details, and at a pace that let me consider and visualize each step in my mind without having to constantly pause or rewind the video. Thanks a lot for your amazing work! :)

[adityapillai]

Clear, concise, and overall easy to understand for a newbie like me. Thanks!

[ai_lite]

great expalation! Good for you! Don't stop giving ML guides!

[drelvenkee]

The best video so far. The animation is easy to follow and the explaination is very straight forward.

[thecheekychinaman]

The best ViT explanation available. Also key to understand this for understanding Dino and Dino V2

[sheikhshafayat]

Man, you made my day! These lectures were golden. I hope you continue to make more of these

[valentinfontanger]

Amazing, I am in a rush to implement vision transformer as an assignement, and this saved me so much time !

[aimeroundiaye]

Amazing video. It helped me to really understand the vision transformers. Thanks a lot.

[thepresistence]

15 minutes of heaven 🌿. Thanks a lot understood clearly!

[vladik]

Very good explanation, better that many other videos on YouTube, thank you!

[swishgtv]

This reminds me of Encarta encyclopedia clips when I was a kid lol! Good job mate!

[arash_mehrabi]

Thank you for your Attention Models playlist. Well explained.

[MonaJalal]

This was a great video. Thanks for your time producing great content.

[rajgothi]

You have explained ViT in simple words. Thanks

[DerekChiach]

Thank you, your video is way underrated. Keep it up!

[sehaba]

Thank you so much for this amazing presentation. You have a very clear explanation, I have learnt so much. I will definitely watch your Attention models playlist.

[wengxiaoxiong]

good video, what a splendid presentation, wang shusen yyds.

[nehalkalita]

Nicely explained. Appreciate your efforts.