⚠️ PerceptionMetrics was previously known as DetectionMetrics. The original website referenced in our
Sensorspaper is still available[here]
PerceptionMetrics is a toolkit designed to unify and streamline the evaluation of object detection and segmentation models across different sensor modalities, frameworks, and datasets. It offers multiple interfaces including a GUI for interactive analysis, a CLI for batch evaluation, and a Python library for seamless integration into your codebase. The toolkit provides consistent abstractions for models, datasets, and metrics, enabling fair, reproducible comparisons across heterogeneous perception systems.
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What’s supported in PerceptionMetrics
| Task | Modality | Datasets | Framework |
|---|---|---|---|
| Segmentation | Image | RELLIS-3D, GOOSE, RUGD, WildScenes, custom GAIA format | PyTorch, Tensorflow |
| LiDAR | RELLIS-3D, GOOSE, WildScenes, custom GAIA format | PyTorch (tested with | |
More details about the specific metrics and input/output formats required fow each framework are provided in the Compatibility section
DetectionMetrics
Our previous release, ** DetectionMetrics**, introduced a versatile suite focused on object detection, supporting cross-framework evaluation and analysis.
Cite our workif you use it in your research!
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Cite our work
@article{PaniegoOSAssessment2022,
author = {Paniego, Sergio and Sharma, Vinay and Cañas, José María},
title = {Open Source Assessment of Deep Learning Visual Object Detection},
journal = {Sensors},
volume = {22},
year = {2022},
number = {12},
article-number = {4575},
url = {https://www.mdpi.com/1424-8220/22/12/4575},
pubmedid = {35746357},
issn = {1424-8220},
doi = {10.3390/s22124575},
}