Feature Pyramid Networks (FPN) Explained
Learn how Feature Pyramid Networks build multi-scale feature representations through top-down pathways and lateral connections for robust object detection.
10 min readConcept
Tagged with
Object Detection
Explore machine learning concepts related to Object Detection. Clear explanations and practical insights.
8
Concepts Found
Concepts Related to Object Detection
ASFF: Adaptive Spatial Feature Fusion
Learning where to fuse multi-scale features with per-pixel, per-level fusion weights. ASFF challenges FPN's uniform fusion assumption.
15 min readConcept
RoI Pooling, RoI Align & Deformable RoI Pooling
Understanding region-based feature extraction for object detection, from quantized pooling to sub-pixel alignment and adaptive sampling
12 min readConcept
Anchor-Based vs Anchor-Free Object Detection
Compare anchor-based vs anchor-free object detection: Faster R-CNN and RetinaNet anchors vs FCOS and CenterNet point-based methods.
12 min readConcept
NAS-FPN: Learning to Design Feature Pyramid Networks
Understanding how neural architecture search discovers optimal feature pyramid architectures that outperform hand-designed alternatives
15 min readConcept
DETR Explained: Object Detection with Transformers
Understanding end-to-end object detection with transformers, from DETR's object queries to bipartite matching and attention-based localization
15 min readConcept
NMS & Soft-NMS: Removing Duplicate Detections
Understanding Non-Maximum Suppression algorithms for object detection post-processing, from greedy NMS to soft variants
12 min readConcept
Focal Loss: Focusing on Hard Examples
Learn focal loss for deep learning: down-weight easy examples, focus on hard ones. Interactive demos of gamma, alpha balancing, and RetinaNet.
9 min readConcept