Answers.Body: <p>Understanding segmentation heads: They are essentially the final layers of your model responsible for translating the rich feature representations from the backbone(ViT in this case) into pixel-level class predictions (i.e., the segmentation mask). Several popular types of segmentation heads exist, each with its strengths and considerations:</p>
  <p>Common Segmentation Heads:</p>
  <ol>
  <li>Linear decoder: This is the simplest type of head, where the flattened output of the ViT backbone is fed into a linear layer (or a few) to predict class probabilities for each pixel. It's computationally efficient but might lack the ability to capture fine-grained details.</li>
  <li>MLP Decoders: Similar to the linear decoder, but with multiple layers and potentially non-linearities to allow for more complex feature transformations.</li>
  <li>CNNs: To implement, you need to reshape and interpolate the token outputs into a 2D grid before applying convolutional layers.</li>
  <li>U-Net like decoder:  Inspired by the popular U-Net architecture, these decoders have skip connections that combine low-level (detail-rich) features from the backbone with high-level (semantic-rich) features from the decoder. Transformer tokens can be reshaped and processed through a U-Net-like decoder with skip connections. Check out TransUNet as mention below list of implementations.</li>
  <li>There are other types of decoder too. I covered most basic once.</li>
  </ol>
  <p>The good news is that you're <strong>not restricted to specific segmentation heads</strong> for specific ViT backbones. In principle, any of the segmentation heads mentioned above can be used with any ViT backbone.</p>
  <p>Choosing the Right Head: consider the following factors when choosing a segmentation head:</p>
  <ol>
  <li><p>Complexity and Performance: Linear/MLP heads are the simplest but might not offer the best performance. Transformer-based decoders and mask transformers offer better modeling capabilities but can be more complex.</p>
  </li>
  <li><p>Computational Resources: If computational resources are limited, simpler heads like MLP might be preferable.</p>
  </li>
  <li><p>Task-Specific Requirements: consider the nature of the segmentation task. If you need to capture fine details or long-range dependencies, a more powerful decoder might be necessary.</p>
  </li>
  </ol>
  <p>Example implementations with links (Not in specific order):</p>
  <ol>
  <li><a href="https://towardsdatascience.com/efficient-image-segmentation-using-pytorch-part-4-6c86da083432" rel="nofollow noreferrer">SegFormer</a>: designed to work with transformer backbones.</li>
  <li><a href="https://medium.com/axinc-ai/dpt-segmentation-model-using-vision-transformer-b479f3027468" rel="nofollow noreferrer">DPT</a>: Uses a ViT backbone followed by a dense prediction head.</li>
  <li><a href="https://github.com/zbwxp/SegVit" rel="nofollow noreferrer">SegViT</a>: Semantic degmentation with plain vision transformers</li>
  <li><a href="https://medium.com/@olga.mindlina/vision-transformer-for-semantic-segmentation-on-medical-images-practical-uses-and-experiments-a2e3939d9870" rel="nofollow noreferrer">Swin Transformer</a>: Hierarchical Vision Transformer using Shifted Windows</li>
  <li><a href="https://github.com/Beckschen/TransUNet" rel="nofollow noreferrer">TransUNet</a>: Transformers as encoder. U-net like structure.</li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-06-17T15:38:40.677Z
Answers.Text: Thank you @Kulin-Patel for your detailed response! If I am not restricted to specific segmentation heads for specific ViT backbones, how does the output of the ViT backbone serve as input to the segmentation head of choice? I understand from your response that CNN-based heads require the ViT tokens to be post-processed before serving as input, but is post-processing always required (even with non-CNN heads)? Conceptually, how does ViT output become head input?
Answers.UserDisplayName: Alex
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-06-18T00:15:33.087Z
Answers.Text: Exactly. Not just CNNs, but for any head, You will need to adjust according to the input required for that specific head. Take look at those code base. I have already provided the links. Some of them are articles to learn from.
Answers.UserDisplayName: Kulin Patel
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-06-19T22:48:40.267Z
Answers.Text: It would be great if you could accept the answer if it has answered your question.
Answers.UserDisplayName: Kulin Patel
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Answers.CreationDate: 2024-06-17T04:37:48.330Z
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Answers.UserDisplayName: Kulin Patel
Answers.Body: <p><a href="https://huggingface.co/facebook/sam-vit-base" rel="nofollow noreferrer">Segment Anything Model (SAM) - ViT Base (ViT-B)</a></p>
  <p><a href="https://github.com/facebookresearch/segment-anything" rel="nofollow noreferrer">Segment Anything Model Source Code</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-06-20T20:27:30.387Z
Answers.LastActivityDate: 2024-06-20T20:27:30.387Z
Answers.UserDisplayName: sailfish009
Answers.Body: <p>For a simple segmentation head, you can use the head of <a href="https://github.com/fudan-zvg/SETR" rel="nofollow noreferrer">SETR</a> whose backbone is the ViT. And for more advanced choice, you might modify the decoder head yourself. For other transformer-based segmentation models, a specific decoder head is needed. Like <a href="https://github.com/NVlabs/SegFormer" rel="nofollow noreferrer">SegFormer</a> and <a href="https://github.com/rstrudel/segmenter" rel="nofollow noreferrer">Segmenter</a>.</p>
  
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Answers.UserDisplayName: Yu Yan
Body: <p>I am working with vision transformers (ViT) for the task of image segmentation, but I am unsure of which segmentation head to use.</p>
  <p>I know I need a vision transformer as my backbone, and a segmentation head to generate the image segmentation from the learned representations of the backbone given an input image. Can I use any segmentation head with a ViT backbone, or do certain heads apply to specific ViT backbones?</p>
  <p>Appreciate anyone who can offer some insight!</p>
  
Tags: <image-segmentation><vision-transformer>
Title: Vision transformer for image segmentation
UserDisplayName: Alex
ThreadText: # Vision transformer for image segmentation
  
  (Asked by: username_0 on 2024-06-17)
  
  I am working with vision transformers (ViT) for the task of image segmentation, but I am unsure of which segmentation head to use.
  
  I know I need a vision transformer as my backbone, and a segmentation head to generate the image segmentation from the learned representations of the backbone given an input image. Can I use any segmentation head with a ViT backbone, or do certain heads apply to specific ViT backbones?
  
  Appreciate anyone who can offer some insight!
  
  **Tags:** image-segmentation, vision-transformer
  
  ---
  
  ## 3 answers
  
  ### Answer by username_1 on 2024-06-17. Score: 1 | Accepted answer
  
  Understanding segmentation heads: They are essentially the final layers of your model responsible for translating the rich feature representations from the backbone(ViT in this case) into pixel-level class predictions (i.e., the segmentation mask). Several popular types of segmentation heads exist, each with its strengths and considerations:
  
  Common Segmentation Heads:
  
  1. Linear decoder: This is the simplest type of head, where the flattened output of the ViT backbone is fed into a linear layer (or a few) to predict class probabilities for each pixel. It's computationally efficient but might lack the ability to capture fine-grained details.
  2. MLP Decoders: Similar to the linear decoder, but with multiple layers and potentially non-linearities to allow for more complex feature transformations.
  3. CNNs: To implement, you need to reshape and interpolate the token outputs into a 2D grid before applying convolutional layers.
  4. U-Net like decoder: Inspired by the popular U-Net architecture, these decoders have skip connections that combine low-level (detail-rich) features from the backbone with high-level (semantic-rich) features from the decoder. Transformer tokens can be reshaped and processed through a U-Net-like decoder with skip connections. Check out TransUNet as mention below list of implementations.
  5. There are other types of decoder too. I covered most basic once.
  
  The good news is that you're **not restricted to specific segmentation heads** for specific ViT backbones. In principle, any of the segmentation heads mentioned above can be used with any ViT backbone.
  
  Choosing the Right Head: consider the following factors when choosing a segmentation head:
  
  1. Complexity and Performance: Linear/MLP heads are the simplest but might not offer the best performance. Transformer-based decoders and mask transformers offer better modeling capabilities but can be more complex.
  2. Computational Resources: If computational resources are limited, simpler heads like MLP might be preferable.
  3. Task-Specific Requirements: consider the nature of the segmentation task. If you need to capture fine details or long-range dependencies, a more powerful decoder might be necessary.
  
  Example implementations with links (Not in specific order):
  
  1. [SegFormer](https://towardsdatascience.com/efficient-image-segmentation-using-pytorch-part-4-6c86da083432): designed to work with transformer backbones.
  2. [DPT](https://medium.com/axinc-ai/dpt-segmentation-model-using-vision-transformer-b479f3027468): Uses a ViT backbone followed by a dense prediction head.
  3. [SegViT](https://github.com/zbwxp/SegVit): Semantic degmentation with plain vision transformers
  4. [Swin Transformer](https://medium.com/@olga.mindlina/vision-transformer-for-semantic-segmentation-on-medical-images-practical-uses-and-experiments-a2e3939d9870): Hierarchical Vision Transformer using Shifted Windows
  5. [TransUNet](https://github.com/Beckschen/TransUNet): Transformers as encoder. U-net like structure.
  
  > **Comments:**
  > **username_0**: Thank you @Kulin-Patel for your detailed response! If I am not restricted to specific segmentation heads for specific ViT backbones, how does the output of the ViT backbone serve as input to the segmentation head of choice? I understand from your response that CNN-based heads require the ViT tokens to be post-processed before serving as input, but is post-processing always required (even with non-CNN heads)? Conceptually, how does ViT output become head input?  
  > **username_1**: Exactly. Not just CNNs, but for any head, You will need to adjust according to the input required for that specific head. Take look at those code base. I have already provided the links. Some of them are articles to learn from.  
  > **username_1**: It would be great if you could accept the answer if it has answered your question.  
  
  ---
  
  ### Answer by username_2 on 2024-06-20. Score: 0
  
  [Segment Anything Model (SAM) - ViT Base (ViT-B)](https://huggingface.co/facebook/sam-vit-base)
  
  [Segment Anything Model Source Code](https://github.com/facebookresearch/segment-anything)
  
  
  ---
  
  ### Answer by username_3 on 2024-06-25. Score: 0
  
  For a simple segmentation head, you can use the head of [SETR](https://github.com/fudan-zvg/SETR) whose backbone is the ViT. And for more advanced choice, you might modify the decoder head yourself. For other transformer-based segmentation models, a specific decoder head is needed. Like [SegFormer](https://github.com/NVlabs/SegFormer) and [Segmenter](https://github.com/rstrudel/segmenter).
  
  
  ---
  
  