2 votes
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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 ...
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  • 1,833
2 votes
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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 ...
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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 ...
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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 ...
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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 ...
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1 vote
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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 ...
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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 ...
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