Thanks :)

Glad it was helpful!

Thank you so much!

I am in undergrad and you know its so difficult to actually understand and more so to implement these things. This guy is a saviour.

Coming soon! :)

You are most welcome

It was rather the x that comes from the lower early layers and it has better spatial information. Also, the skip connection is x, not g.

Glad to hear that!

Glad it was helpful!

Thank you very much.

Glad you think so!

ok :)

Concordo

AFAIK they should have the same number of features. Because to get g, you send the layer from which g is coming from(the layer right beneath the skip connection) through a conv layer which halves the number of filters

Welcome!

The output shape is defined by the stride and not by the kernel size of the convolution. Here is a few lines of code if anyone wats to experiment. In fact, these types of snippets can be fun and educational. import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D model = Sequential() model.add(Conv2D(1, kernel_size=(2,2), padding='same', strides=(1,1), input_shape = (256, 256,1))) print(model.summary())

That would be the content I'd be covering next week. Please wait until July 14th.

You can use any image processing software that lets you paint over your image and then save the overlay as a mask. These masks are your manual segmented results. For example, you can use APEER. May be the first few minutes of this video can help you understand the process. kzfaq.info/get/bejne/nq-dmdiqsZjFgKc.html

Glad it helped!

I know this is a bit old, but yea I'm pretty sure he got it backwards. I'm looking at my implementation of RESUNET++ and the bridge block outputs more channels not reduces.

Glad you liked it

@@DigitalSreeni Yes !! Sir also try to make videos on Optimization of features extracted using any transfer learning models with BAT , Gray wolf optimizer or any one else. Thanks again

I am not sure what you mean by 0.2 < 0.1. If you are referring to my example of aligned large weights get larger whereas aligned small weights get smaller, then may be I could have used better words to explain. In summary, if both weights are large then you get a large sum. If they both are small then you get a smaller sum value. If one is large and one is large, then you get something in between. Therefore, the final weights can reflect the attention given to objects of interest as the weights would be aligned at these locations. In the video, I did not say 0.2 < 0.1, I said, the sum would be small. What I missed was to say, 'the sum would would be small, in comparison to the sum of aligned weights.' I thought that was implied, but apparently not. Thanks for pointing it out.

In an attention U-Net, the output of the attention mechanism is typically a weighting factor (also called attention coefficients or attention maps) that is used to modulate the feature maps of the U-Net. This weighting factor is computed by passing the input feature maps through a set of convolutional layers and then applying a softmax activation to obtain a set of values between 0 and 1 that represent the importance of each feature map. To use the attention weighting factor to modulate the feature maps, we need to multiply it with the original feature maps. In other words, we want to scale each feature map by its corresponding attention coefficient. This can be done using the Keras multiply layer, which multiplies two tensors element-wise. In the case of the attention U-Net, the two tensors that we want to multiply are the upsampled attention coefficients (upsample_psi) and the feature maps of the U-Net (x). We want to multiply these two tensors element-wise to obtain a set of scaled feature maps that take into account the attention coefficients. This is expressed as y = layers.multiply([upsample_psi, x]). We don't want to multiply the attention coefficients with the gating signal g, because the gating signal is used to compute the attention coefficients and is not itself a set of feature maps. The gating signal g is used to modulate the feature maps at a later stage in the network, after the attention mechanism has been applied. Specifically, the gating signal g is concatenated with the upsampled and modulated feature maps to produce the final output of the attention U-Net.

Hi! As I understand, this step is required to transform data acquired by summing X and G, which both are 64 x 64 x 128 at this point (128 being the number of features for each pixel) into a matrix of weights for each pixel, which must be 64 x 64 x 1 (1 being the weight of each pixel). So after applying the ReLu you need a way to 'squash' these 128 features to just 1 value - the weight of the current pixel. And this is exactly what convolution with n_filters = 1 does.

All are different architectures. Transformers have scaled dot product attention which doesn't have convolution layers. Whereas U-Net is convolution based model.

In my code, the attention mechanism is implemented using both channel-wise attention and spatial attention. The attention mechanism is applied at multiple levels during the upsampling process. Channel-wise attention is incorporated through the use of gating signals and attention blocks. The gating_signal function creates a channel-wise gating signal by applying a 1x1 convolution to the input. The attention block (attention_block function) performs channel-wise attention by combining information from the downsampled and upsampled paths. Spatial attention is achieved through the use of upsampling layers and concatenation operations.

You can import a model from segmentation models and deconstruct and reconstruct using attention. It involves some work and I do not recommend it as I am not convinced about the real effectiveness of attention.

In general, I find simple U-net to be more effective and efficient compared to many of its variants. R2Unet and Attention Unet may give you better results based on the type of details in your objects and background. Unfortunately, there are no papers I am aware of that talk about when R2Unet/Attention/ etc. will work better and when they fail. In summary, what you found out about R2Unet and its attention equivalent is possible.

@@DigitalSreeni Thank you.

I am not sure, I haven't thought about that concept. May be there is some literature out there.

Spatial information in the context of Attention U-Net refers to the spatial relationships between elements of an image, such as pixels or objects. The Attention U-Net uses attention mechanisms to dynamically weight different regions of an image based on their spatial information. This allows the network to focus on the most relevant features for each task and improve accuracy of image segmentation tasks.

This is the intro video, the coding video will be out on July 14th., in a couple of days.