Object detection evaluation metrics using Python.

Last update: Sep 06, 2022

Overview

Object detection evaluation metrics

Prerequisites
How to use
Author

Current features

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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Object detection evaluation metrics using Python.

Related tags

Overview

Object detection evaluation metrics

Table of contents

Current features

Prerequisites

How to use

Author

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Releases(v1.0.0)

Owner

Louis Facun

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