Body: <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>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-13T13:22:20.447Z
Comments.Text: 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.
Comments.UserDisplayName: Brale
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-13T13:33:20.363Z
Comments.Text: 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?
Comments.UserDisplayName: Ugurcan
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-13T13:48:30.350Z
Comments.Text: Yes it would be but the encoder is still EfficientNet.
Comments.UserDisplayName: Brale
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-13T16:41:43.877Z
Comments.Text: Which dataset are they suggesting to train these models on?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-13T17:59:02.180Z
Comments.Text: 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.
Comments.UserDisplayName: Ugurcan
Tags: <machine-learning><deep-learning><ai-design><tensorflow><image-segmentation>
Title: How to compare SegNet, U-Net and EfficientNet?
UserDisplayName: Ugurcan
ThreadText: # 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. 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.  
  
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  ## 0 answers
  
  