Object Detection
12 tutorialsFrom sliding windows to transformer-based detectors
A structured progression through object detection — from task taxonomy and evaluation metrics, through the R-CNN family, SSD and RetinaNet, YOLO architectures, transformer-based DETR, and finishing with end-to-end training, error analysis, and deployment workflows.
Prerequisites: Python & NumPy basics · CNN fundamentals · Basic OpenCV
All Tutorials
Image Classification, Localization & Object Detection
Establish the full task taxonomy — classification, localization, and detection — and survey the benchmark datasets and output formats that underpin every detector covered in this series.
Bounding Boxes, IoU, NMS & mAP
Build the complete evaluation toolkit from scratch: box format conversions, IoU computation, Non-Maximum Suppression, Precision-Recall curves, and mean Average Precision.
Sliding Window, Selective Search & R-CNN
Trace the path from brute-force sliding window detectors through selective search region proposals to the original R-CNN, and understand the bottlenecks that each approach introduced.
Faster R-CNN: Region Proposal Network
Dissect the Faster R-CNN architecture: how the RPN shares backbone features, generates anchors, scores objectness, regresses box deltas, and feeds proposals into the detection head.
SSD, RetinaNet & Focal Loss
Study two one-stage detectors that differ in how they handle class imbalance: SSD with hard negative mining and RetinaNet with focal loss — and understand why focal loss is a more principled solution.
YOLO Architecture: Backbone, Neck & Head
Master the full YOLO design — from the grid-cell prediction of v1 to the CSP backbone, PANet neck, and decoupled detection head of YOLOv8 — and understand every loss component.
YOLO Training on a Custom Dataset
End-to-end workflow: annotate images in YOLO format, configure a training run, monitor loss curves, evaluate with COCO metrics, and tune augmentations for your domain.
OD 8A: DETR Transformer Detection
Understand how DETR replaces anchors, NMS, and RPN with a set-prediction transformer: encoder-decoder architecture, learned object queries, and bipartite Hungarian matching.
OD 8B: RF-DETR Transformer Detection
Part B extends transformer-based detection to RF-DETR, emphasizing practical training, inference, evaluation, and comparison with standard DETR-style detectors.
OD 9A: Evaluation, Error Analysis & Deployment with YOLO
Run standardised COCO evaluation, diagnose failure modes with per-category and per-size breakdowns, draw Precision-Recall and F1 curves, and package the model for edge deployment.
OD 9B: Evaluation, Error Analysis & Deployment with RF-DETR
Part B repeats the final evaluation workflow for RF-DETR, focusing on transformer-detector error patterns, metric comparison, qualitative diagnosis, and deployment readiness.
Pseudo Annotation Using SAM 3
Use SAM 3 as an annotation assistant for object-detection datasets: generate high-quality masks from prompts, convert masks into bounding boxes, filter weak pseudo labels, and export reviewed annotations for YOLO or COCO training.