Dear Aarohi Your channel is very knowledgeable & helpful for all Artificial Intelligence/ Data Scientist Professionals. Stay blessed & keep sharing such a good content.
Hello Ma’am Your AI and Data Science content is consistently impressive! Thanks for making complex concepts so accessible. Keep up the great work! 🚀 #ArtificialIntelligence #DataScience #ImpressiveContent 👏👍
The SWIN Transformer is designed for visual recognition tasks, particularly suited for processing high-resolution images efficiently. It breaks down images into patches and processes them hierarchically, making it useful for tasks like classification, object detection, and segmentation.
we have already done the patch partitioning. So, after the window shift, pixels in one local window may come from several local windows that are not adjacent. What should we do? so a masking mechanism is employed to limit self-attention computation to within each sub-window. Then the computed values are return to their original positions.
Hello, mam I'm working on the diagnosis of skin diseases and I have done implementation by using Vit, so I wanna ask if should i use Swin transformer and concatenate both Vit and Swin transformer together to make novelty? I need your suggestion, please.
Yes, you can experiment with combining ViT and Swin Transformer using ensemble methods to potentially improve skin disease diagnosis, but carefully evaluate individual performances and consider computational resources.
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.
Awesome madam, everytime eagrly waiting yours video, way of explanation is very clear and every one can understood easily Waiting for implementation video also One request I saw yours all gans videos, but if possible can you make conditional dcgan implementation video for any color images. Happy learning
I understand you studied it thoroughly, but can you implement the transformer, I am asking because I myself find it difficult to create this transformers in papers. Can you? Do you? Good work btw .
Great content on SOTA model architectures! Thank you
Glad you liked it!
Dear Aarohi
Your channel is very knowledgeable & helpful for all Artificial Intelligence/ Data Scientist Professionals. Stay blessed & keep sharing such a good content.
Best video ever!! Clear explanations. Thanks a lot.
Glad it was helpful!
Really very helpful video Ma'am to understand the concept of Swin Transformer.
Glad to hear that
Worth watching it again
Hello Ma’am
Your AI and Data Science content is consistently impressive! Thanks for making complex concepts so accessible. Keep up the great work! 🚀 #ArtificialIntelligence #DataScience #ImpressiveContent 👏👍
My pleasure 😊
Great explanation. Keep adding new videos :)
Thank you, I will
muy bueno el contendio de tus tutoriales, gracias x compartir
Glad my content is helpful!
Great video! Congratulations!
Glad you liked it!
Thank you soo much mam for this amazing video
Most welcome 😊
You are just amazing by explaining it so simple
Thank you so much 😀
mam can you please share me the link where i can get pre traine weights for swin transformers
Nice. Is this like the new way for vision transformers, or is this specific for certain tasks?
The SWIN Transformer is designed for visual recognition tasks, particularly suited for processing high-resolution images efficiently. It breaks down images into patches and processes them hierarchically, making it useful for tasks like classification, object detection, and segmentation.
Thanks for the clear explanation. I just couldn't understand how the Masked MSA works. If I could find out, I'll write back.
we have already done the patch partitioning. So, after the window shift, pixels in one local window may come from several local windows that are not adjacent. What should we do? so a masking mechanism is employed to limit self-attention computation to within each sub-window. Then the computed values are return to their original positions.
Thanks a lot, I can't believe there is just 40 likes!!!!!!!
Glad video was helpful!
superb explanation
Thank you so much 🙂
Wow, great explanation...... 👍
Glad it helped
Nicely Explained..!
Thank you!
This was extremely helpful thank you very much. I subscribed.
Thanks for the sub!
Hello, mam I'm working on the diagnosis of skin diseases and I have done implementation by using Vit, so I wanna ask if should i use Swin transformer and concatenate both Vit and Swin transformer together to make novelty? I need your suggestion, please.
Yes, you can experiment with combining ViT and Swin Transformer using ensemble methods to potentially improve skin disease diagnosis, but carefully evaluate individual performances and consider computational resources.
Hi, have you done the ensemble of vit and swin transformer?
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.
I will try to make it but bit busy for few days
Thank you
@@CodeWithAarohi
Keep up the good work😊
Thank you, I will
Awesome madam, everytime eagrly waiting yours video, way of explanation is very clear and every one can understood easily
Waiting for implementation video also
One request I saw yours all gans videos, but if possible can you make conditional dcgan implementation video for any color images.
Happy learning
Will try to do a video soon.
Thank you
Awesome
Thanks
Amazed
Hi mam, If possible do a video on how to implement Meta-DeTR
Sure
I understand you studied it thoroughly, but can you implement the transformer, I am asking because I myself find it difficult to create this transformers in papers. Can you? Do you? Good work btw .
Next video will be a implementation of Swin Transformer.
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