Evaluate detections with SAHI and COCO metrics — evaluate_model

Runs SAHI inference across the validation set described by a COCO-style annotation file and computes COCO metrics using pycocotools. The function returns a tidy summary of the standard 12 bbox metrics alongside the raw prediction table for further analysis.

Usage

evaluate_model_sahi(
  model,
  annotation_json,
  image_dir = NULL,
  use_slicing = TRUE,
  slice_size = 512,
  overlap = 0.2,
  max_images = NULL,
  save_predictions = NULL,
  iou_type = "bbox",
  max_dets = 100
)

Arguments

model: A PetrographyModel from from_pretrained().
annotation_json: Path to COCO annotation JSON (e.g. valid/_annotations.coco.json).
image_dir: Directory containing the images referenced in the annotation file. If NULL, image paths are resolved relative to the annotation file.
use_slicing: Whether to use SAHI sliced inference (default TRUE).
slice_size: Slice size for SAHI inference (pixels, default 512).
overlap: Overlap ratio between slices (default 0.2).
max_images: Optional maximum number of images to evaluate (useful for smoke tests).
save_predictions: Optional path to write COCO-format predictions JSON.
iou_type: IoU type to evaluate ("bbox" by default).
max_dets: Maximum detections per image for evaluation. For dense detection (100+ objects), set to 300 or higher (default: 100).

Value

A list with elements summary (tibble of COCO metrics), predictions (tibble of detections), and coco_eval (pycocotools object).