Single-stage detector
An object-detection architecture that predicts object locations and class labels directly in one integrated model pass without a separate region-proposal stage.
Deep Learning with R, Third Edition
A single-stage object detector predicts object locations and categories directly in one integrated model pass rather than first generating region proposals.
- How it is used
- Supports efficient object detection for real-time and resource-constrained applications.
- How deep it goes
- Explains proposal avoidance, direct outputs, grid prediction, and multi-scale features, while simplifying YOLO to one box per grid cell.
- Examples
- Uses a simplified YOLO grid, pretrained ResNet, 448 × 448 inputs, and RetinaNet inference on a COCO-trained painting example.
- What you can do after
- Build it from scratch with a convolutional backbone and prediction head, or apply it through a pretrained RetinaNet detector.
Read how Deep Learning with R, Third Edition explains single-stage detector →
Deep Learning with Python, Third Edition
An object-detection architecture that predicts bounding boxes and class labels in one pass with a single model, rather than using separate proposal and classification stages.
- How it is used
- Deploys object detection when fast, efficient localization and classification matter more than pixel-level masks.
- How deep it goes
- Explains joint outputs, grid prediction, confidence, and backbones through a simplified YOLO build, without developing the full original architecture.
- Examples
- Uses counting, tracking, and cropping applications plus a grid-based YOLO model with ResNet and KerasHub.
- What you can do after
- Build a simplified YOLO detector from scratch using a pretrained ConvNet backbone and separate box and class outputs.
Read how Deep Learning with Python, Third Edition explains single-stage detector →
Related concepts
- RetinaNetincludes · RetinaNet is a one-stage object-detection architecture that uses convolutional feature pyramids with separate classification and bounding-box regression heads.
- YOLOincludes · YOLO is a family of single-stage object-detection models that predict object classes and locations directly from an image in one inference pass.
- object detection modelkind of · An object detection model is a computer-vision model that identifies object instances and predicts their classes and spatial locations in visual data.
- object detectionaddresses · Object detection is a computer-vision task that identifies object instances in visual data, assigns them categories, and estimates their locations.
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