Medical image segmentation is an innovative process that enables surgeons to have a virtual "x-ray vision." It is a highly valuable tool in healthcare, providing non-invasive diagnostics and in-depth ...
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Weighted Boxes Fusion in Object Detection: A Comparison with Non-Maximum Suppression
Weighted box fusion: The post-processing step is a trivial yet important component in object detection. In this article, we will demonstrate the significance of Weighted Boxes Fusion (WBF) as opposed ...
Enhancing Medical Multi-Label Image Classification Using PyTorch & Lightning
In the pivotal field of medical diagnostics, swift and accurate image classification plays a crucial role in aiding healthcare professionals' decision-making. The advent of deep learning, coupled with ...
PaddlePaddle: Exploring Object Detection, Segmentation, and Keypoints
PaddlePaddle: Welcome to our guide of machine learning frameworks, where we'll examine PaddlePaddle, TensorFlow, and PyTorch. Recent benchmark tests have revealed PaddlePaddle as a potential ...
Train YOLO NAS on Custom Dataset
YOLO-NAS is currently the latest YOLO object detection model. From the outset, it beats all other YOLO models in terms of accuracy. The pretrained YOLO-NAS models detect more objects with better ...
Train YOLOv8 Instance Segmentation on Custom Data
Image segmentation is a core vision problem that can provide a solution for a large number of use cases. Starting from medical imaging to analyzing traffic, it has immense potential. Instance ...
