{ "id": 425901, "title": "📌 A byte sized primer on choosing the right segmentation model 🖼️", "url": "/competitions/google-research-identify-contrails-reduce-global-warming/discussion/425901", "author_name": "Suraj", "post_date": "2023-07-20T21:05:26.246000", "votes": 9, "comment_count": 1, "views": 0, "content": "

1. Start with UNet or UNet++: These models have been widely used and have mostly shown good performance in various segmentation tasks.

\n

2. Complex Problem Statements: If the problem statement involves complex segmentation tasks with multiple or small objects then UNet++, DeepLab v3 or DeepLabV3+ might be more suitable due to their ability to capture detailed features.

\n

3. Efficiency : If It is of concern, then FPN(Feature Pyramid Networks) can be worthy as they tend to balance accuracy and computational resources.

\n

4. Attention mechanisms: Models with attn. mechanisms like MAnet(Multi Attention Networks) or PAN(Pyramid Attention Networks) can be relevant if you want the model to focus on important regions and suppress irrelevant features.

\n

5. Multi scale features :If the image has multi scale content then PSPNet(Pyramid Scene Parsing Network), FPN or DeepLabV3 which incorporate pyramid pooling or dilated convolutions can be worth a shot.

\n

References 📕

\n
    \n

  1. U-Net:

    \n
    2. \n

  2. U-Net++:

    \n
    3. \n

  3. DeepLabv3:

    \n
    4. \n

  4. DeepLabv3++:

    \n
    5. \n

  5. FPN (Feature Pyramid Network):

    \n
    6. \n

  6. MAnet:

    \n
    7. \n

  7. PAN (Pyramid Attention Network):

    \n
    8. \n

  8. PSPNet (Pyramid Scene Parsing Network):

    \n
    9. \n
", "messages": [ { "id": 2352355, "postDate": "2023-07-20T21:05:26.247Z", "content": "

1. Start with UNet or UNet++: These models have been widely used and have mostly shown good performance in various segmentation tasks.

\n

2. Complex Problem Statements: If the problem statement involves complex segmentation tasks with multiple or small objects then UNet++, DeepLab v3 or DeepLabV3+ might be more suitable due to their ability to capture detailed features.

\n

3. Efficiency : If It is of concern, then FPN(Feature Pyramid Networks) can be worthy as they tend to balance accuracy and computational resources.

\n

4. Attention mechanisms: Models with attn. mechanisms like MAnet(Multi Attention Networks) or PAN(Pyramid Attention Networks) can be relevant if you want the model to focus on important regions and suppress irrelevant features.

\n

5. Multi scale features :If the image has multi scale content then PSPNet(Pyramid Scene Parsing Network), FPN or DeepLabV3 which incorporate pyramid pooling or dilated convolutions can be worth a shot.

\n

References 📕

\n
    \n

  1. U-Net:

    \n
    2. \n

  2. U-Net++:

    \n
    3. \n

  3. DeepLabv3:

    \n
    4. \n

  4. DeepLabv3++:

    \n
    5. \n

  5. FPN (Feature Pyramid Network):

    \n
    6. \n

  6. MAnet:

    \n
    7. \n

  7. PAN (Pyramid Attention Network):

    \n
    8. \n

  8. PSPNet (Pyramid Scene Parsing Network):

    \n
    9. \n
", "rawMarkdown": "**1. Start with UNet or UNet++**: These models have been widely used and have mostly shown good performance in various segmentation tasks. \n\n**2. Complex Problem Statements**: If the problem statement involves complex segmentation tasks with multiple or small objects then UNet++, DeepLab v3 or DeepLabV3+ might be more suitable due to their ability to capture detailed features.\n\n**3. Efficiency :** If It is of concern, then FPN(Feature Pyramid Networks) can be worthy as they tend to balance accuracy and computational resources.\n\n**4. Attention mechanisms:** Models with attn. mechanisms like MAnet(Multi Attention Networks) or PAN(Pyramid Attention Networks) can be relevant if you want the model to focus on important regions and suppress irrelevant features.\n\n**5. Multi scale features :**If the image has multi scale content then PSPNet(Pyramid Scene Parsing Network), FPN or DeepLabV3 which incorporate pyramid pooling or dilated convolutions can be worth a shot.\n\n\n## References 📕\n\n1. **U-Net**:\n - Paper Link: ![](https://arxiv.org/abs/1505.04597)\n\n2. **U-Net++**:\n - Paper Link: ![](https://arxiv.org/abs/1807.10165)\n\n3. **DeepLabv3**:\n - Paper Link: ![](https://arxiv.org/abs/1706.05587)\n\n4. **DeepLabv3++**:\n - Paper Link: ![](https://arxiv.org/abs/1802.02611)\n\n5. **FPN (Feature Pyramid Network)**:\n - Paper Link: ![](https://arxiv.org/abs/1612.03144)\n\n6. **MAnet**:\n - Paper Link: ![](https://arxiv.org/abs/2009.02130)\n\n7. **PAN (Pyramid Attention Network)**:\n - Paper Link: ![](https://arxiv.org/abs/1805.10180)\n\n8. **PSPNet (Pyramid Scene Parsing Network)**:\n - Paper Link: ![](https://arxiv.org/abs/1612.01105)\n\n\n", "votes": 8 }, { "id": 2355916, "postDate": "2023-07-23T18:04:29.137Z", "content": "

What loss functions do use guys?
\nDice Loss beats BCE and Focal Loss for me on cross validation.

", "rawMarkdown": "What loss functions do use guys?\nDice Loss beats BCE and Focal Loss for me on cross validation.", "votes": 1 } ], "comments": [ { "id": 2355916, "author_name": "iadduk", "author_url": "", "post_date": "2023-07-23T18:04:29.137000", "content": "

What loss functions do use guys?
\nDice Loss beats BCE and Focal Loss for me on cross validation.

", "votes": 1, "replies": [] } ], "raw_markdown_by_id": { "2352355": "**1. Start with UNet or UNet++**: These models have been widely used and have mostly shown good performance in various segmentation tasks. \n\n**2. Complex Problem Statements**: If the problem statement involves complex segmentation tasks with multiple or small objects then UNet++, DeepLab v3 or DeepLabV3+ might be more suitable due to their ability to capture detailed features.\n\n**3. Efficiency :** If It is of concern, then FPN(Feature Pyramid Networks) can be worthy as they tend to balance accuracy and computational resources.\n\n**4. Attention mechanisms:** Models with attn. mechanisms like MAnet(Multi Attention Networks) or PAN(Pyramid Attention Networks) can be relevant if you want the model to focus on important regions and suppress irrelevant features.\n\n**5. Multi scale features :**If the image has multi scale content then PSPNet(Pyramid Scene Parsing Network), FPN or DeepLabV3 which incorporate pyramid pooling or dilated convolutions can be worth a shot.\n\n\n## References 📕\n\n1. **U-Net**:\n - Paper Link: ![](https://arxiv.org/abs/1505.04597)\n\n2. **U-Net++**:\n - Paper Link: ![](https://arxiv.org/abs/1807.10165)\n\n3. **DeepLabv3**:\n - Paper Link: ![](https://arxiv.org/abs/1706.05587)\n\n4. **DeepLabv3++**:\n - Paper Link: ![](https://arxiv.org/abs/1802.02611)\n\n5. **FPN (Feature Pyramid Network)**:\n - Paper Link: ![](https://arxiv.org/abs/1612.03144)\n\n6. **MAnet**:\n - Paper Link: ![](https://arxiv.org/abs/2009.02130)\n\n7. **PAN (Pyramid Attention Network)**:\n - Paper Link: ![](https://arxiv.org/abs/1805.10180)\n\n8. **PSPNet (Pyramid Scene Parsing Network)**:\n - Paper Link: ![](https://arxiv.org/abs/1612.01105)\n\n\n", "2355916": "What loss functions do use guys?\nDice Loss beats BCE and Focal Loss for me on cross validation." } }