75 - Image Segmentation using U-Net - Part 3 (What are trainable parameters?)

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DigitalSreeni

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Comments 75 - Image Segmentation using U-Net - Part 3 (What are trainable parameters?)

75 - Image Segmentation using U-Net - Part 3 (What are trainable parameters?)

Rajeshwar Sehdev 2 months ago

How we calculated 2320 weights? in second layer

kevin chen 2 months ago

Hello， how can i get the picture of model structure? Appreciate!

Andres Bergsneider 2 months ago

@DigitalScreeni I'm a little late in joining the comments sections here. First off, thanks again for sharing this. Is so educational and informative!

I have a question in regards to the initial # of feature maps and been looking to get some guidance/clarification. Where are the 16 initial feature maps coming from? Is it from the original image after running it through 16 different "randomized" filters/kernels? I've seen multiple variations of U-Net and this number varies depending on developers preference. Thanks in advance!

Asish Binu Mathew 2 months ago

Words cannot describe how much your content has helped me. Your videos should have more views. Keep posting please

Rethinker Media 2 months ago

What does 16 mean in 256x256x16? Is it like channels?

Rethinker Media 2 months ago

I think you got the biases wrong at 2:30

Masoud Fara 2 months ago

great videos.tnx

Zeeshan Patel 2 months ago

I am not sure why, but my code compiles and shows me that I have 1,879,665 total parameters. There are 0 Non-trainable parameters. Is there a reason why my number is less than yours?