Vision Transformer for Image Classification

Excellent explanation 👌

You have explained ViT in simple words. Thanks

Nice video!!, Just a question what is the argue behind to rid of the vectors c1 to cn, and just remain with c0? Thanks

This is a werid question but what if we made the transformer try to learn the positional encoding by itself? We give it everything but the positional encoding and we tell it to find the positional encoding. What would it learn and how would that end up helping us? I dont know if this thought will go anywhere I wanted to throw it out here.

Nicely explained. Appreciate your efforts.

Thank you for the clear explanation!!☺

Thank you. Best ViT video I found.

In the job market, do data scientists use transformers?

amazing precise explanation

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

👏

Thank you for your Attention Models playlist. Well explained.

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

great

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! :)

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

Awesome Explanation.
Thank you

This is a great explanation video.
One nit : you are misusing the term 'dimension'. If a classification vector is linear with 8 values, that's not '8-dimensional' -- it is a 1-dimensional vector with 8 values.

Brilliant. Thanks a million

WHY is the transformer requiring so many images to train?? and why is resnet not becoming better with ore training vs ViT?

If we ignore output c1 ... cn, what c1 ... cn represent then?

Very good explanation
subscribed!

这英语也是醉了

其实我觉得up主说中文更好🥰🤣

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

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.

really great explaination , thankyou

great video!

Not All Heroes Wear Capes <3

Brilliant explanation, thank you.

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

Great explanation

great video. thanks. could u plz explain swin transformer too?

Very clear, thanks for your work.

The simplest and more interesting explanation, Many Thanks. I am asking about object detection models, did you explain it before?

Wonderful talk

Good job! Thanks

Very clear, thanks for your work.

Really good, thx.

Very nice job, Shusen, thanks!

Great great great

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

Great explanation :)

The concept has similarities to TCP protocol in terms of segmentation and positional encoding. 😅😅😅

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

If you remove the positional encoding step, the whole thing is almost equivalent to a CNN, right?
I mean those dense layers are just as filters of a CNN.

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

Amazing video. It helped me to really understand the vision transformers. Thanks a lot. But i have a question why we only use token cls for classifier .