Image Classification Using Vision Transformer | ViTs

Step by Step Implementation explained : Vision Transformer for Image Classification

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In 2020, Google Brain team introduced a Transformer-based model that can be used to solve an image classification task called Vision Transformer (ViT). Its performance is very competitive in comparison with conventional CNNs on several image classification benchmarks.

Vision transformer (ViT) is a transformer used in the field of computer vision that works based on the working nature of the transformers used in the field of natural language processing.

#transformers #computervision

[잇준-vm]

I'm student learning AI in Korea, your video helps me a lot, thanks for good material!
i'll try ViT for another image data.
please keep upload your video

[ashimasingla]

Dear Aarohi
Your channel is very knowledgeable & helpful for all Artificial Intelligence/ Data Scientist Professionals. Stay blessed & keep sharing such a good content.

[debjitdas]

Very well explained, Madam, how to get the confusion matrix and other metrics such as f-1 score, precision, recall? How to check actually which test samples are detected correctly and which are not?

[shivamgoel]

very nice explanation! Patch Size, data loader of loading the images, resizing them and converting to tensors, efficient loading by giving batch size to optimize memory usage and more :)

[muhammadmujtaba-ai]

Best vid for ViT
The way you explained each step and coding part, that is awsm.
Currently I am applying the gained knowledge on a new type of dataset.
Thank you for such a detailed video.

[sohambhowal]

Hi, thank you so much for this tutorial. Where can I find the flowers dataset from?

[jhinaouiroudayna]

Nice video! Have you tried working with hyperspectral datasets like Indian Pines that got more than 3 channels (about 200)?

[sanjoetv]

please make a landmark detection here in vision transformer. i greatly in need for this project to be finished and the task is to create a 13 landmark detection using vision transformer. and i cant find any resources that teaches how to do a landmark detection if vision transformer. this channel is my only hope.

[growwithfuyad]

Can you add the performance matrix codes with gradcam analysis, as well as other versions of Vits and swing?

[lotfiamr]

very nice video but you did not explain what "going_modular.going_modular import engine" it is and where you got it from ??

[zahranematzadeh]

Thanks for your video. Does ViT work for non-square images? is it better to use the pretrained ViT for our specific task, right?

[feiyangbai]

Hello Aarohi, thank you for this great video. But I had going_modular error, and helper_functions error. I know my colab version is different from yours, I even try to change to the version you showed in the video, it still reported the same problem saying cannot find the model. I try to install the 2 libraries, but still had the errors. Any suggestions?

Thank you.

[smitshah]

Thanks for a great tutorial. But I am facing an issue that when I change the image, it is displaying the newer image but the predicted class label and probability are not getting updated.

[AshutoshKumar-lpxl]

It's very clear conceptual explanation, very rare. Keep teaching us.

[kvenkat]

Nice explanation mam but i am beginner of vits so i want customized the vit as per my need so what type parameters I need to chage in standard model specially for image classification

[vishnusit]

Make speical video on how to improve accuracy and avoid overfitting with solution example for VIT.. thses are most common problem for all i guess..

[MahaveerTirumalasetty-bb]

mam u r teaching standards are next level mam

[soravsingla]

Code with Aarohi is Best YouTube channel for Artificial Intelligence #CodeWithAarohi

[AIinAgriculture]

Thank you for your videos. Along with accuracy, I wish know precision, recall and F1 score too. Could you please include precision, recall and F1 score metrics evaluation code.

[sayeemmohammed]

Mam, could you please provide me the custom dataset that you've used on the video?
From your provided link, I couldn't find the exact dataset.