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2 votes
Accepted

How does Mask R-CNN automatically output a different number of objects on the image?

Object detection models usually generate multiple detections per object. Duplicates are removed in a post-processing step called Non-Maximum Suppression (NMS). The ...
Kostya's user avatar
  • 2,534
2 votes
Accepted

What does the "number of channels" correspond to in U-Net?

In this example you have a gray scale image of size 572x572 and 1 (gray) channel. The first convolution operation consists of 64 filters of size 3x3 and 1 channel per filter. The channel of the ...
legammler's user avatar
2 votes
Accepted

How should I incorporate numerical and categorical data as part of the inputs to the U-net for semantic segmentation?

What you want to do is called multi-task learning. Here's what you do: Create a second Input. Attach it to 1D CNN (2-3 layers), so it aggregates this tabular information. Concatenate this feature ...
Abhishek Verma's user avatar
2 votes

How to identify and diferentiate several edge lines of an object?

I don't think that more advanced AI would necessarily produce more consistent results. Check something as simple as the Prewitt operator, which is pretty damn good at edge detection. I would suggest ...
ImotVoksim's user avatar
2 votes
Accepted

Why do we do need compression in Semantic Segmentation?

There is never a 100% accurate theory, however it's been observed to be beneficial, however I would argue that is due to the following: you want to have a latent dimension, to learn the manifold ...
Alberto's user avatar
  • 2,173
1 vote

How can I use Artificial Intelligence to compare to paragraphs to see if they share the same meaning?

There are a lot of document embedding libraries that you can use. They will represent each paragraph as a vector and you can use the distance between these vectors as a proxy for similarity. You will ...
Noah Mancino's user avatar
1 vote
Accepted

How does mixing and matching encoders and decoders work in image segmentation?

It's possible to mix and match all sorts of encoders and decoders. If the output of the encoder can be mapped to the input of the decoder, and a loss function can be backpropagated through the model, ...
Robin van Hoorn's user avatar
1 vote

How does the classification head of EfficientDet work?

The classification head works as follows. After the stack of BiFPN we have a feature map of size B x C x H x W. For EfficientDet ...
spiridon_the_sun_rotator's user avatar
1 vote

Dissection of a depth map

Depth maps are created using principles of photometry (method of measuring light). The depth maps (rather images) you took from the website are "images" not exact depth "maps". So ...
Arun Aniyan's user avatar
1 vote
Accepted

What is the difference (if any) between semantic segmentation and multi-class, mutually exclusive classification?

Image/object classification (or recognition) (Multi-class) image/object classification (or recognition) typically refers to the task of assigning one label to an image, so we typically assume that ...
nbro's user avatar
  • 40.8k
1 vote
Accepted

Why does my model not improve when training with mini-batch gradient descent, while it does with Adam?

Well, some time ago I also faced the same issue in the semantic segmentation task. Batch normalization is expected to improve convergence, because the normalization of activations prevents the ...
spiridon_the_sun_rotator's user avatar

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