CSE445Computer Vision
Tutorials/Object Detection/YOLO Architecture: Backbone, Neck & Head
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OD-06Intermediate 120 min

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.

PyTorchUltralytics YOLOv8OpenCV

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. 1YOLOv1: 7×7 grid, B box predictions, C classes per cell, single forward pass
  2. 2YOLOv3: Darknet-53 backbone, 3 detection scales, anchor clustering with k-means
  3. 3YOLOv5/v8 backbone: CSP bottleneck blocks and C2f modules
  4. 4Neck: PANet path aggregation for bottom-up and top-down feature fusion
  5. 5Decoupled head: separate classification and regression branches; CIoU + BCE losses

Learning Objectives

  • Explain grid-based prediction and the difference between objectness score and class probability
  • Describe how PANet differs from FPN and why bidirectional feature flow helps
  • Identify backbone, neck, and head in a YOLOv8 architecture diagram
  • Run YOLOv8 across model sizes (n/s/m/l/x) and plot the speed–accuracy trade-off

Key Takeaways

  • YOLOv1 predicts B boxes and C class scores per cell — 7×7×(B×5+C) output tensor
  • CSP blocks halve the gradient path, reducing memory and improving training efficiency
  • PANet aggregates features from all scales bottom-up then top-down — YOLO's multi-scale secret
  • CIoU loss penalises centre distance and aspect-ratio difference on top of IoU — faster convergence

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 YOLOv8n, YOLOv8s, and YOLOv8m on a test video; measure and plot FPS vs. mAP@50
  2. 2Visualise YOLOv8 feature maps at the three detection scales using hooks
  3. 3Reimplement the CIoU loss for a single predicted/target pair in PyTorch
  4. 4Export YOLOv8m to ONNX and benchmark latency with ONNX Runtime vs. native PyTorch

Tools & Libraries

PyTorchUltralytics YOLOv8OpenCV
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