DeepLab models, first debuted in ICLR ‘14, are a series of deep learning architectures designed to tackle the problem of semantic segmentation. After making iterative refinements through the years, ...
The Future of Image Recognition is Here: PyTorch Vision Transformers
Welcome to the second part of our series on vision transformer. In the previous post, we introduced the self-attention mechanism in detail from intuitive and mathematical points of view. We also ...
Meet YOLO-NAS: New YOLO Object Detection Model Beats YOLOv6 & YOLOv8
Developing a new YOLO-based architecture can redefine state-of-the-art (SOTA) object detection by addressing the existing limitations and incorporating recent advancements in deep learning. Deep ...
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 ...
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 ...
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 ...
