🔍

Fine-Tuning YOLO v5 for Custom Datasets

Feb 6, 2025

Lecture Notes: Fine-Tuning YOLO v5 with Custom Dataset

Introduction

  • YOLO v5: You Only Look Once (YOLO) is a real-time object detection system. The video discusses fine-tuning YOLO v5 using a custom dataset.
  • Controversy: There is ongoing controversy regarding the naming of YOLO v5, with debates available on platforms like Hacker News.
  • Implementation: YOLO v5 by Ultralytics is implemented in PyTorch and is considered efficient among YOLO versions.

YOLO Versions and Real-Time Object Detection

  • Comparison:
    • YOLO v5 is compared to YOLO v4 and EfficientDet. While EfficientDet has better performance metrics, YOLO v5 offers higher frame rates which are beneficial for real-time applications.
    • YOLO models are one-stage detectors, which focus on real-time speed.
    • Two-stage detectors like Faster R-CNN offer more accuracy at the cost of speed.
  • Leaderboards: Real-time object detection metrics from Papers with Code and comparisons with other detectors like EfficientDet.

Installing and Setting Up YOLO v5

  • Environment Setup:
    • Use Google Colab with a Tesla P100 GPU.
    • Install necessary dependencies including PyTorch, COCO API, and Apex (for mixed precision computation).
  • Apex: Recommended for speeding up computations using mixed precision techniques.
  • Dataset Preparation:
    • Convert and sort class categories for consistent model training.

Fine-Tuning YOLO v5

  • Cloning Repository:
    • Git clone Ultralytics YOLO v5 repo and use specific checkpoints for reproducibility.
  • Model Selection:
    • Choose model (YOLO v5x) based on parameters and required performance.
  • Configuration:
    • Modify configurations like number of classes and anchor points in model YAML files.
  • Training Process:
    • Train the model with custom specifications (e.g., images at 640px, batch size of 4, 30 epochs).
    • Use CUDA and Apex for efficient training.
    • Results and weights stored after training for further use.

Results and Evaluation

  • Evaluation and Metrics:
    • Plot training metrics like precision, recall, and classification loss.
    • Checkpoint with best mean Average Precision (MAP) saved.
  • Inference:
    • Run inference on test images to evaluate model performance.
    • YOLO v5 demonstrated good accuracy on unseen images and custom dataset.

Conclusion

  • Summary: The fine-tuned YOLO v5 model shows promising results for real-time object detection with custom datasets.
  • Future Steps:
    • Next video will cover deploying the trained model on mobile devices.
    • Building a simple mobile app to utilize the trained YOLO v5 model.

Closing

  • Watch the next video to learn about deploying YOLO v5 on mobile devices.
  • Encouragement to like, share, and subscribe.

Full transcript