ifcb_classify_images: Classify Pre-Extracted IFCB PNG Images Using a Gradio Application

Description

Classifies one or more pre-extracted IFCB PNG images through a CNN model served by a Gradio application. Each PNG is uploaded to the Gradio server and the prediction result is returned as a data frame. Per-class F2 optimal thresholds are applied automatically; predictions scoring below the threshold for their class are labeled "unclassified" in class_name.

Usage

ifcb_classify_images(
  png_file,
  gradio_url = "https://irfcb-classify.hf.space",
  top_n = 1,
  model_name = "SMHI NIVA ResNet50 V5",
  verbose = TRUE
)

Value

A data frame with the following columns:

file_name: The PNG file name of the classified image.
class_name: The predicted class name with per-class thresholds applied; "unclassified" if the score is below the threshold.
class_name_auto: The winning class name without any threshold applied (argmax of scores).
score: The prediction confidence score (0–1).
model_name: The name of the CNN model used for classification.

Images that could not be classified have NA in class_name, class_name_auto, and score. When top_n > 1, multiple rows are returned per image (one per prediction).

Arguments

png_file: A character vector of paths to PNG files to classify.
gradio_url: A character string specifying the base URL of the Gradio application. Default is "https://irfcb-classify.hf.space", which is an example Hugging Face Space with limited resources intended for testing and demonstration. For large-scale classification, deploy your own instance of the classification app (source code: https://github.com/EuropeanIFCBGroup/ifcb-inference-app) and pass its URL here.
top_n: An integer specifying the number of top predictions to return per image. Default is 1 (top prediction only). Use Inf to return all predictions.
model_name: A character string specifying the name of the CNN model to use for classification. Default is "SMHI NIVA ResNet50 V5". Use ifcb_classify_models() to list all available models.
verbose: A logical value indicating whether to print progress messages. Default is TRUE.

Details

To classify all images in a raw IFCB sample (.roi file) without first extracting them manually, use ifcb_classify_sample() instead.

Examples

Run this code

if (FALSE) {
# Classify a single pre-extracted PNG
result <- ifcb_classify_images("path/to/D20220522T003051_IFCB134_00001.png")

# Classify several PNGs at once
pngs <- list.files("path/to/png_folder", pattern = "\\.png$",
                   full.names = TRUE)
result <- ifcb_classify_images(pngs, top_n = 3)
}