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Computer Vision Experimentation Frameworks - Generalized-YOLOv5

Authors: Karol Gotkowski

Keywords: object detection, deep learning, YOLO, out-of-the-box, pytorch-implementation, natural image analysis, biomedical image analysis

Generalized-YOLOv5 is a modified version of YOLOv5. YOLOv5 itself is a realtime object detection framework designed for natural images. Our Generalized-YOLOv5 version has two crucial extensions. First, an extension that generalizes YOLOv5 also to non-natural images, which enables the usage of state-of-the-art realtime object detection to many domains (e.g. the medical domain). Second, an integration of N-fold cross-validation ensembles into the framework, improving the performance especially in low data regimes. Both contributions have been thoroughly tested in a series of experiments on a X-ray nodule dataset. All experiments alongside the insights discovered based on the experiments are included in the documentation.

Computer Vision Experimentation Frameworks - Generalized-YOLOv5 Image
License
GPL-3.0

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