CSE445Computer Vision
Tutorials/Self-Supervised Learning for Computer Vision/Label-Efficient SSL-YOLO26 Comparative Study
Previous tutorialDINOv3-Guided Feature Distillation into YOLO26
SSL-OD-05Advanced / Research 3 Hours

Label-Efficient SSL-YOLO26 Comparative Study

Perform a controlled comparison between supervised and self-supervised YOLO26 detectors. Students evaluate how SimCLR, BYOL, I-JEPA, and DINOv3-guided pretraining affect object-detection performance when only a limited percentage of bounding-box annotations is available.

PyTorchUltralyticsPandasMatplotlibpycocotools

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 CoveredStudy each concept as part of the complete pipeline: understand its role, implement it, and verify its effect during transfer to YOLO26.
  1. 1Label efficiencyMeasure how much detection quality is retained when only a fraction of the available annotations is used.
  2. 2Controlled experimentsChange the initialization method while keeping data, schedule, augmentation, and evaluation settings fixed.
  3. 3Fixed train, validation, and test splitsReuse identical partitions across every run to prevent data selection from confounding the comparison.
  4. 4Multi-seed evaluationRepeat experiments with several random seeds and report variation rather than relying on one favorable run.
  5. 5mAP@0.5 and mAP@0.5:0.95Report both lenient single-threshold accuracy and stricter COCO-style performance averaged across IoU thresholds.
  6. 6Precision and recallSeparate false-positive behavior from missed detections to explain changes hidden by a single aggregate score.
  7. 7Per-class average precisionInspect whether SSL transfer helps all categories consistently or mainly benefits particular visual classes.
  8. 8Convergence speedCompare learning curves and the number of epochs required to reach a stable validation result.
  9. 9Inference latencyConfirm that pretraining improves initialization without unintentionally changing the deployed detector's runtime cost.
  10. 10Robustness analysisEvaluate predictions under difficult scale, lighting, occlusion, and background conditions to identify reliable gains.
  11. 11Annotation-cost reductionTranslate label-efficiency results into the practical amount of manual bounding-box annotation that may be avoided.
  12. 1210%, 25%, 50%, and 100% label budgetsRun the same protocol at four annotation levels to reveal where representation pretraining provides the strongest benefit.

Learning Objectives

  • Construct controlled 10%, 25%, 50%, and 100% label splits
  • Compare random, official, SimCLR, BYOL, I-JEPA, and DINOv3-guided YOLO26 initializations
  • Report accuracy, convergence, robustness, and latency without unsupported claims
  • Produce research-style tables, curves, and failure analysis

Key Takeaways

  • Only the initialization strategy changes; data splits and training settings stay controlled
  • Label-efficiency curves reveal how performance changes with annotation budget
  • Conclusions must be based on measured results rather than assumed SSL gains

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

Hands-On Exercises

  1. 1Run all six YOLO26 initialization strategies across each label budget
  2. 2Create label-efficiency curves with multi-seed uncertainty summaries
  3. 3Generate per-class results and a detection failure-case gallery
  4. 4Write a recommendation grounded in accuracy, annotation budget, and latency

Tools & Libraries

PyTorchUltralyticsPandasMatplotlibpycocotools
Previous tutorialDINOv3-Guided Feature Distillation into YOLO26