⚠️ DetectionMetrics v1 website referenced in our Sensors paper is still available here
What is DetectionMetrics?
DetectionMetrics is a family of toolkits designed to unify and streamline the evaluation of perception models across different frameworks and datasets. Looking for our published DetectionMetrics v1? Check out all the relevant links below.
Now, we’re excited to introduce DetectionMetrics v2! While retaining the flexibility of our previous release, DetectionMetrics has been redesigned with an expanded focus on image segmentation, with plans to extend support to object detection and LiDAR applications. As we move forward, v2 will be the actively maintained version, featuring continued updates and enhancements to keep pace with evolving AI and computer vision technologies.
💻 Code | 🔧 Installation | 🧩 Compatibility | 📖 Docs |
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What’s supported in DetectionMetrics
Task | Modality | Datasets | Framework |
---|---|---|---|
Segmentation | Image | Rellis3D, GOOSE, custom GAIA format | PyTorch, Tensorflow |
LiDAR | Coming soon | Coming soon | |
Object detection | Image | Check DetectionMetrics v1 | Check DetectionMetrics v1 |
More details in the Compatibility section.
DetectionMetrics v1
Our previous release, DetectionMetrics v1, introduced a versatile suite focused on object detection, supporting cross-framework evaluation and analysis. Cite our work if you use it in your research!
💻 Code | 📖 Docs | 🐋 Docker | 📰 Paper |
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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},
}