NoCohen66/Verification4ObjectDetection
🩺🛣️ IBP IoU an approach for the formal verificaion of object detection models.
When you're using AI models for object detection, especially in critical applications like autonomous vehicles or medical imaging, you need to be sure they're robust against minor changes to input images. This project helps evaluate how stable your object detection model is by checking if its bounding box predictions (Intersection over Union, or IoU) remain reliable even when the image has small distortions like brightness, contrast, or noise. This is for AI engineers or researchers who build and deploy object detection systems and need to formally verify their robustness.
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Use this if you need to rigorously test how small changes to an image's brightness, contrast, or noise affect your object detection model's accuracy, specifically focusing on the Intersection over Union metric.
Not ideal if you are looking for a general-purpose object detection model or a tool for data labeling, rather than formal robustness verification.
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Jupyter Notebook
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LGPL-3.0
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Last pushed
Jun 07, 2024
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