Object detection evaluation metrics using Python.

Related tags

Deep Learning computer-vision recall object-detection confusion-matrix precision object-detection-metrics
Overview

Object detection evaluation metrics

Table of contents

Current features

  • Confusion matrix
  • Precision
  • Recall
  • F1 Score
  • mAP (COCO, Pascal voc etc.)

Prerequisites

  • Python
  • Numpy pip install numpy

How to use

Prepare ground truth and prediction files

  • Each "image" should have separate text files.

  • Use same names for both ground truth and predictions.

  • Use separate folder for both ground truth and predictions.

  • Each file should be in this format: xmin ymin xmax ymax label_id

  • Example (label id starts with 0):

1 2 3 4 0
1 2 3 4 1
1 2 3 4 1

Example code

>> pred_dir = Path(Path.cwd(), "examples", "pred") >>> odem = ObjectDetectionEval(true_dir, pred_dir, labels=["cat", "dog"]) >>> odem.confusion_matrix() >>> odem.classification_report() predictions true cat dog None Total cat 1 0 0 1 dog 0 1 3 4 None 2 0 0 2 Total 3 1 3 precision recall f1-score cat 1.00 0.33 0.50 dog 0.25 1.00 0.40"> 
>>> from pathlib import Path
>>> from odem import ObjectDetectionEval

>>> true_dir = Path(Path.cwd(), "examples", "true")
>>> pred_dir = Path(Path.cwd(), "examples", "pred")

>>> odem = ObjectDetectionEval(true_dir, pred_dir, labels=["cat", "dog"])
>>> odem.confusion_matrix()
>>> odem.classification_report()
        predictions
true     cat       dog       None      Total
cat       1         0         0         1
dog       0         1         3         4
None      2         0         0         2

Total     3         1         3
      precision     recall    f1-score
cat       1.00      0.33      0.50
dog       0.25      1.00      0.40

Author

  • Louis Philippe Facun
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