Model Card: yolov10x_bb_detect_model
Model Details
- Model Name:
yolov10x_bb_detect_model
- Model Type: Single-Class Object Detection and Feature Extractor
- Description: This model is designed to detect the presence of bark beetles in images. It identifies and places a bounding box around the target but does not classify different species of bark beetles. It operates under the single class label: 'bark_beetle'.
Evaluation Datasets
To understand the model's capabilities, its performance was tested on two different types of datasets:
- In-Distribution (ID): This dataset contains images that are similar to the data the model was trained on. Performance on this dataset shows how well the model performs on familiar types of images.
- Out-of-Distribution (OOD): This dataset contains images that are intentionally different species from the training data.
Performance
Object Detection
The model's ability to correctly identify and locate bark beetles is measured by its mean Average Precision (mAP). This metric evaluates both the accuracy of the bounding box placement and the classification confidence. The score is averaged over multiple Intersection over Union (IoU) thresholds, from 50% overlap (0.50) to 95% overlap (0.95), providing a comprehensive view of prediction accuracy. A higher mAP score indicates better performance.
| Dataset | mAP (0.50 : 0.95) | Notes |
|---|
| In-Distribution (ID) | 0.9589 | Exhibits extremely strong detection performance on images similar to its training data. |
| Out-of-Distribution (OOD) | 0.9369 | Maintains high accuracy even on novel species, demonstrating robust generalization. |
Feature Extraction (Embedding Performance)
The model can also convert images into numerical representations (embeddings). The quality of these embeddings is evaluated by how well they group similar species together in a feature space.
Internal Cluster Validation
These metrics measure the quality of the clusters formed by the embeddings without referring to ground-truth labels. They assess how dense and well-separated the clusters are.
| Metric | ID Score | OOD Score | Interpretation |
|---|
| Silhouette Score | 0.6974 | 0.4194 | Measures how similar an object is to its own cluster compared to others. Higher is better (closer to 1). The ID embeddings form better-defined clusters. |
| Davies-Bouldin Index | 0.3653 | 0.3248 | Measures the average similarity between each cluster and its most similar one. Lower is better (closer to 0). The OOD embeddings show slightly less overlap between clusters. |
| Calinski-Harabasz Index | 7684.23 | 799.045 | Measures the ratio of between-cluster dispersion to within-cluster dispersion. Higher is better. The ID embeddings form significantly denser and more separated clusters. |
External Cluster Validation
These metrics evaluate the clustering performance by comparing the results to the true species labels.
| Metric | ID Score | OOD Score | Interpretation |
|---|
| Adjusted Rand Index (ARI) | 0.1859 | 0.0050 | Measures the similarity between true and predicted labels, correcting for chance. Higher is better (closer to 1). |
| Normalized Mutual Info (NMI) | 0.4846 | 0.2716 | Measures the agreement between the clustering and the true labels. Higher is better (closer to 1). |
| Cluster Purity | 0.2988 | 0.1281 | Measures the extent to which clusters contain a single class. Higher is better (closer to 1). |
Conclusion: The external validation scores are low for both datasets, indicating the model's feature representations do not effectively separate different species of bark beetles on their own.
Phylogenetic Correlation (Mantel Test)
This test determines if the model's learned features correlate with the evolutionary relationships (phylogeny) between different bark beetle species.
- Mantel R-statistic: This value ranges from -1 to 1. A positive value means species that are close in the model's feature space are also close evolutionarily. A value near zero indicates no correlation.
- p-value: Indicates the statistical significance of the result. A p-value below 0.05 typically suggests a significant correlation.
| Dataset | Mantel R-statistic | p-value | Interpretation |
|---|
| In-Distribution (ID) | -0.1164 | 0.2730 | There is no statistically significant correlation between the model's feature embeddings and the species' evolutionary history. |
| Out-of-Distribution (OOD) | -0.0493 | 0.5860 | There is no statistically significant correlation for the OOD data either. |
