Implementation fo Monte Carlo Dropblock for Epistemic Uncertainty Estimation in YOLO11

[animi10]

Hello!

I’m working on integrating a Monte Carlo DropBlock into the YOLO11 architecture to estimate epistemic uncertainty during inference.
What I’ve Done:

Module Integration:

• I’ve implemented the MCDropBlock class in block.py and integrated it into the .yaml configuration file.

• I’ve successfully trained the model with my implementation. Here’s a snippet of the MCDropBlock implementation:

    class MCDropBlock(nn.Module):
        """
        Monte Carlo DropBlock implementation.
        """
        def __init__(self, c1, c2, drop_prob=0.1, block_size=7):
            super().__init__()
            self.c1 = c1
            self.c2 = c2
            self.block_size = block_size
            self.drop_prob = drop_prob
            self.conv = nn.Conv2d(c1, c2, 1) if c1 != c2 else nn.Identity()

        def forward(self, x):
            x = self.conv(x)
            gamma = self._compute_gamma(x)

            # Generate stochastic DropBlock mask
            mask = (torch.rand(x.shape[0], *x.shape[2:]) < gamma).float().to(x.device)
            block_mask = self._compute_block_mask(mask)

            # Apply mask
            out = x * block_mask[:, None, :, :]
            out = out * block_mask.numel() / block_mask.sum()

            return out

        def _compute_block_mask(self, mask):
            block_mask = F.max_pool2d(input=mask[:, None, :, :],
                                      kernel_size=(self.block_size, self.block_size),
                                      stride=(1, 1),
                                      padding=self.block_size // 2)
            if self.block_size % 2 == 0:
                block_mask = block_mask[:, :, :-1, :-1]
            return 1 - block_mask.squeeze(1)

        def _compute_gamma(self, x):
            return self.drop_prob / (self.block_size ** 2)

Epistemic Uncertainty Estimation:

• The epistemic uncertainty estimation needs to occur at inference time by performing multiple stochastic forward passes (Monte Carlo sampling).

• To do this, I need the model to remain in training mode during inference to allow dropout.

my problem:

Inference Workflow:

• I cannot use YOLO’s default predict method because it disables training behaviors during inference.
• My plan is to implement a custom predictor (inheriting from BasePredictor) that works with the raw model outputs before final detections.

Flexibility:

• I need a flexible pipeline that allows me to experiment with different stages of the model for integrating the Monte Carlo DropBlock.

What I Need Help With:

Advice on Strategy:

• Does my plan of creating a custom predictor make sense for uncertainty estimation? Are there better approaches to modify YOLO’s inference pipeline?

Raw Model Outputs:

• How do I correctly work with raw outputs (before NMS or final detections) in YOLO’s pipeline while preserving flexibility?

Any insights or suggestions would be greatly appreciated!

[UltralyticsAssistant]

👋 Hello @animi10, thank you for your detailed post and for exploring innovative directions with Ultralytics YOLO 🚀! Implementing Monte Carlo DropBlock for epistemic uncertainty estimation sounds like a fascinating project 💡.

We encourage you to review the Ultralytics Docs for helpful information on custom predictors and how to work with the YOLO inference pipeline. Many of the modules in YOLO are designed with flexibility in mind, so you might find some useful starting points there.

For your 🥷 Custom Inference Workflow:

• Designing a custom predictor by inheriting from BasePredictor is a valid strategy 👍. It provides a clean way to override behavior while leveraging existing YOLO utilities. Ensure you follow the modular structure to maintain compatibility with other parts of the pipeline!
• For working with raw model outputs before NMS or detections, you can delve into components like the Forward method in the model or predictors to interact with intermediate outputs directly.

If you believe there might be an issue in the current implementation that prevents this, could you provide a minimum reproducible example (MRE)? This will help us analyze and potentially pinpoint any blockers or offer precise guidance 🛠️.

🚀 Upgrade

Please verify that you’re using the latest version of the ultralytics package, as it ensures compatibility and may include features that can assist with your modifications. Update as shown below:

pip install -U ultralytics

Additional Resources

If you want to discuss ideas with the community or collaborate:

• Join the Ultralytics Discord for real-time discussions 🎧
• Dive into discussions on Discourse for broader questions 📚
• Share and learn with the community on Subreddit

Environments and Testing

We recommend testing your modifications in a reproducible environment such as:

• Google Colab, Gradient, or Kaggle Notebooks to ensure ease of collaboration and debugging. These platforms come pre-configured with CUDA and PyTorch:
  

Finally, please note that this is an automated response 🤖—an Ultralytics engineer will review your discussion and provide additional feedback soon. Good luck with your implementation, and thank you for contributing to the innovation of YOLO! 🚀

[glenn-jocher]

@animi10 your approach of creating a custom predictor inherited from BasePredictor to handle Monte Carlo DropBlock for epistemic uncertainty estimation is logical and aligns well with YOLO's modular design. This strategy provides the flexibility to perform stochastic forward passes and access raw model outputs before NMS or final detections.

To work with raw outputs, you can utilize the model.model attribute directly during inference. This bypasses post-processing steps like NMS while preserving the flexibility to experiment with intermediate outputs. Ensure that your custom predictor maintains the model in training mode during inference by explicitly calling model.train().

For guidance, you can refer to the BasePredictor implementation in the Ultralytics repo. If further customization is needed, modifying the forward pass logic in your custom predictor will allow precise control over the workflow. Let me know if you need clarification on any specific part!