CSE445Computer Vision
Tutorials/Object Detection/OD 9B: Evaluation, Error Analysis & Deployment with RF-DETR
Previous tutorialOD 9A: Evaluation, Error Analysis & Deployment with YOLONext tutorialPseudo Annotation Using SAM 3
OD-09BIntermediate 90 min

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.

RF-DETRpycocotoolsPyTorchPandasMatplotlib

Code Structure Note

These notes are provided only to help students understand the code structure. Students should review, optimize, and adapt the code before using it in assignments, projects, or experiments.

Topics Covered
  1. 1RF-DETR validation outputs: prediction JSON, confidence scores, and class-wise metrics
  2. 2COCO-style evaluation for transformer-based detector results
  3. 3YOLO vs. RF-DETR error comparison: missed detections, false positives, and localization errors
  4. 4Confidence threshold selection for RF-DETR deployment scenarios
  5. 5Deployment readiness checklist: export format, inference latency, memory use, and reproducibility

Learning Objectives

  • Run RF-DETR evaluation and interpret mAP@50, mAP@50-95, precision, recall, and F1
  • Compare RF-DETR and YOLO failure cases using the same validation images
  • Plot RF-DETR confidence and F1 curves to select a deployment threshold
  • Prepare a compact evaluation report with quantitative metrics and qualitative examples

Key Takeaways

  • RF-DETR and YOLO often fail differently, so paired error analysis is more useful than a single leaderboard score
  • Transformer detectors may improve difficult context-heavy examples while still needing careful threshold tuning
  • Deployment decisions should include latency, memory, preprocessing, and postprocessing, not only mAP
  • A final report should combine tables, curves, and visual examples to explain model behavior

Tutorial Infographic

Notebook Resources

Jupyter Notebook

Student action area

Use this section to practice the tutorial code. Kaggle opens the prepared notebook, Colab lets you experiment in your own runtime, and the .ipynb download is best for local editing or submission.

Open in Kaggle Open in Colab Download .ipynb
Kaggle: follow the official tutorial notebook.
Colab: modify and rerun quickly.
IPYNB: keep a local copy.

Hands-On Exercises

  1. 1Run RF-DETR on the same validation set used for OD 9A and export prediction results
  2. 2Create a YOLO vs. RF-DETR metrics table with mAP50, mAP50-95, precision, recall, F1, and latency
  3. 3Build a failure-case gallery showing examples where RF-DETR wins, YOLO wins, and both fail
  4. 4Write a deployment recommendation for one target scenario using evidence from the metrics and gallery

Tools & Libraries

RF-DETRpycocotoolsPyTorchPandasMatplotlib
Previous tutorialOD 9A: Evaluation, Error Analysis & Deployment with YOLONext tutorialPseudo Annotation Using SAM 3