Vision Transformers (ViT) Explained + Fine-tuning in Python

You are an inspiration james

The is by far the best explanation of the paper that I could find. Thanks a lot!

This video is really helful. Thank you!

Well, Bag of Words and Bag of Visual Words WAS a merger of NLP and Computer Vision, back in the day (2010s)

Thanks a lot for this ! Amazing amazing explanation!

hey, thanks a lot. i have come from TensorFlow. so can u please answer, is it training the whole vit model for our dataset or freezing the vit pre trained part and training classification head only (like trainable=false in tf)?

Very good introductory video. Thanks for sharing.

Really enjoyed every bit. Trying to setup the transformer for an Audio Regression task, the ViT has shown amazing performance in classification

Thank you for the effort ur putting here in your explanations. :)

Great video! I've watched quite a few videos and read papers about Transformers, but your video really made me understand the concept

no fun using huggingface transformers library. you should have explained vision transformers using a more basic implementation, than a high level library

James is the top G in deep learning

it currently gives the error 'no module named 'datasets', anybody has a fix?

Are there such thing that is similar to word embeddings? Or you simply take your pixel data as patches and run it through the dense layer to get projections?

thank you sir

Great video man cheers! Do you have a video about using a dataset made up of your own images on the vision transformer?

Thanks a lot for the video. I cant find any precise explanation about the function of self-attention layer and MLP layer in the encoder modules. Could you maybe add some information about that?

Nice one..! This content desperately needs "Promo SM"!

Another great video of yours. So clear and clarifying. Thx !

Great explanation, unique on YT. Thanks!

great stuff!

In this video, there is no explanation for the output of a vision transformer. In NLP transformers, the output is a probability distribution over the vocab but in vision transformers, I guess it is over a code book. But what this code book is and how it is aligned to the input image is not clear. Thanks a lot for this video but it is incomplete

Just discovered your channel, great stuff! Thanks man! Great explanation and visualisation

The clarity of your discourse is unmatched and it's always a pleasure to follow your videos. Kinda a side effect of your passion for the domain!?

Incredible content! Thx James!

Thank you so much for this video :)