Optimization and Evaluation Query for Ensembling YOLOv8 Segmentation Models

Discussion
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Context: I have developed five building footprint extraction models for 5 different countries using YOLOv8 Instance Segmentation, and now I’m attempting to ensemble them using a weighted fusion method. The code I’m currently using performs the fusion of masks and bounding boxes based on weighted scores from multiple models.

Here is the link to the GitHub repository with the complete implementation.

Questions:

  1. Optimization: Am I applying the weighted fusion method correctly in my current implementation? Are there any optimizations or improvements I could make to enhance performance or efficiency?
  2. Evaluation Metrics: In YOLOv8, I can directly obtain precision (P), recall (R), mean average precision (mAP), and mAP at IoU 0.5 (mAP@0.5) from the predictions. However, after ensembling the models using weighted fusion, how can I compute these metrics for both bounding boxes and masks? If you could provide code snippets for this, it would be highly helpful.
  3. Alternative Methods: Are there any other methods more efficient or suitable than weighted fusion for ensembling YOLOv8 models, particularly for the task of building footprint extraction? Any recommendations on this would be appreciated.

Any insights, suggestions, or code examples would be greatly appreciated!

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Hi there!

It’s fantastic to see your work on ensembling YOLOv8 models for building footprint extraction across different countries. Let’s dive into your questions:

1. Optimization

Your approach to weighted fusion is a solid method for ensembling. However, there are always ways to optimize. Here are a few suggestions:

  • Threshold Tuning: Experiment with different confidence thresholds for each model before fusion. This can help in filtering out low-confidence predictions.
  • Weight Adjustment: Ensure that the weights assigned to each model are reflective of their individual performance. You might want to use validation metrics to determine these weights dynamically.
  • Parallel Processing: If not already implemented, consider parallelizing the inference process for each model to speed up the ensemble predictions.

2. Evaluation Metrics

After ensembling, you can compute the evaluation metrics for both bounding boxes and masks using the following approach:

from ultralytics import YOLO

# Load your ensembled model
model = YOLO("path/to/ensembled_model.pt")

# Run evaluation
results = model.val(data="path/to/your/dataset.yaml")

# Print specific metrics
print("Class indices with average precision:", results.ap_class_index)
print("Average precision for all classes:", results.box.all_ap)
print("Average precision:", results.box.ap)
print("Average precision at IoU=0.50:", results.box.ap50)
print("Class-specific results:", results.box.class_result)
print("F1 score:", results.box.f1)
print("Mean average precision:", results.box.map)
print("Mean average precision at IoU=0.50:", results.box.map50)
print("Mean average precision at IoU=0.75:", results.box.map75)
print("Mean precision:", results.box.mp)
print("Mean recall:", results.box.mr)
print("Precision:", results.box.p)
print("Recall:", results.box.r)

For detailed insights on these metrics, you can refer to our Model Evaluation Insights Guide.

3. Alternative Methods

While weighted fusion is effective, here are a couple of alternative methods you might consider:

  • NMS (Non-Maximum Suppression) Fusion: Apply NMS across the predictions from all models to merge overlapping boxes and masks.
  • Stacking: Use the outputs of your models as features for a meta-model (e.g., a simple neural network or a decision tree) that learns to combine these predictions optimally.
  • Voting Mechanism: Implement a majority voting system where the final prediction is based on the majority agreement among the models.

Each method has its pros and cons, so it might be worth experimenting to see which one works best for your specific use case.

Feel free to explore these options and tweak your implementation. If you encounter any issues or have further questions, don’t hesitate to ask. Happy coding! :rocket:

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You should be able to create a model ensemble by passing multiple weights files as a list to the YOLO class.