FCOS: Fully Convolutional One-stage Object Detection is an anchor-free (anchorless) object detector. It solves object detection problems in a per-pixel prediction fashion, similar to segmentation. ...
The Ultimate Guide to DeepLabv3 – With PyTorch Inference
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 ...
