ibbi_yolov9_od

Model Card: yolov9e_bb_detect_model

Model Details

• Model Name: yolov9e_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'.

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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.

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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.

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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.

External Cluster Validation

These metrics evaluate the clustering performance by comparing the results to the true species labels.

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.