Foundations of Object Detection from an Expert's Overview
summary
A practical walkthrough that trains and evaluates a YOLO pipeline on a pothole dataset using Ultralytics on Google Colab, including code to prepare data, create a YAML, and run YOLOv8 Nano training. The session uses a 6,962 train / 271 val split, shows inference at ~29 ms/image, and reports Precision 0.6, Recall 0.4, mAP 0.4 — a working model with modest accuracy. Comes with visualization, confusion matrix, and saved weights for reuse; follow-along code is provided.
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chapters (19)
- 0:00YOLO object detection intro
- 1:46Introduction
- 2:28Object detection fundamentals
- 7:00Real-world detection challenges
- 8:49YOLO accuracy and efficiency
- 10:13YOLO evolution V1 to V9
- 16:40YOLO V8 architecture
- 19:11How YOLO detects objects
- 24:27Performance trade-offs
- 26:33YOLO V8 training on Colab
- 32:05Q&A on YOLO
- 35:18Pothole dataset prep
- 48:25YOLO V8 training config
- 51:10YOLO V8 model variants
- 53:18Training YOLO V8 Nano
- 55:48Evaluating YOLO model
- 1:00:23Confusion matrix and metrics
- 1:06:50YOLO inference and predictions
- 1:12:20Summary
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