<p>SegNet and U-Net are created for segmentation problem and EfficientNet is created for classification problem. I have a task and it is saying that train these models on the same dataset and compare results. Is it possible?</p>

CC BY-SA 4.0
2020-06-13T13:22:20.447Z
Segmentation is a pixel-wise classification so you can use any classification network with added deconvolution or upsampling layers to upscale to original input resolution.
Brale
CC BY-SA 4.0
2020-06-13T13:33:20.363Z
I see but it seems little bit not make sense because if I add some deconvolutional layers wouldn't the original Efficient model change? If I add deconvolutional layer it would called like "X Segmentation Model based on EfficientNet", right?
Ugurcan
CC BY-SA 4.0
2020-06-13T13:48:30.350Z
Yes it would be but the encoder is still EfficientNet.
Brale
CC BY-SA 4.0
2020-06-13T16:41:43.877Z
Which dataset are they suggesting to train these models on?
nbro
CC BY-SA 4.0
2020-06-13T17:59:02.180Z
There is no specific dataset. I was asked to compare these 3 models on the same dataset which I will choose. That's the why I am confused and it seems to not make sense for me. Brale is right.I could add deconvolutional layer to EfficientNet but I think it would not be EfficientNet anymore. For example, encoder part of SegNet is VGG-16 without fully connected layer. However, after adding some deconvolutional part and some other approaches it is called SegNet instead of VGG-16.
Ugurcan
<machine-learning><deep-learning><ai-design><tensorflow><image-segmentation>
How to compare SegNet, U-Net and EfficientNet?
Ugurcan
# How to compare SegNet, U-Net and EfficientNet?

(Asked by: username_0 on 2020-06-13)

SegNet and U-Net are created for segmentation problem and EfficientNet is created for classification problem. I have a task and it is saying that train these models on the same dataset and compare results. Is it possible?

**Tags:** machine-learning, deep-learning, ai-design, tensorflow, image-segmentation

> **Comments:**
> **username_1**: Segmentation is a pixel-wise classification so you can use any classification network with added deconvolution or upsampling layers to upscale to original input resolution.  
> **username_0**: I see but it seems little bit not make sense because if I add some deconvolutional layers wouldn't the original Efficient model change? If I add deconvolutional layer it would called like "X Segmentation Model based on EfficientNet", right?  
> **username_1**: Yes it would be but the encoder is still EfficientNet.  
> **username_2**: Which dataset are they suggesting to train these models on?  
> **username_0**: There is no specific dataset. I was asked to compare these 3 models on the same dataset which I will choose. That's the why I am confused and it seems to not make sense for me. username_1 is right.I could add deconvolutional layer to EfficientNet but I think it would not be EfficientNet anymore. For example, encoder part of SegNet is VGG-16 without fully connected layer. However, after adding some deconvolutional part and some other approaches it is called SegNet instead of VGG-16.  

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## 0 answers

