Introduction to YOLO V8 from Ultra Analytics

Jul 1, 2024

Lecture: Introduction to YOLO V8 from Ultra Analytics

Overview

  • Host introduces YOLO V8 from Ultra Analytics
  • Discusses the evolution of YOLO models: YOLO V5, V6, V7, and now V8
  • YOLO V8 is an advanced model supporting three types of tasks: detection, segmentation, and classification

Key Features of YOLO V8

  • Developer: Ultra Analytics
  • Tasks Supported:
    • Object Detection
    • Image Segmentation
    • Image Classification
  • Improvement: Faster and more lightweight compared to YOLO V5, V6, and V7
  • Installation: Simplified, no need to clone the entire repository
    • Two usage methods: CLI and Python script

Setup Instructions

  1. Using Google Colab to leverage free GPU
  2. Installation Command: 
    pip install ultralytics
  3. Testing Model: Verify setup using a test image and pre-trained model weights (yolov8n.pt)

Training and Validation

  1. Data Preparation:
    • Organize data directories for training, testing, validation
    • Annotate images using LabelImg tool
    • Generate data.yaml file specifying dataset locations, number of classes, and class names
  2. Running Training: 
    model = YOLO('yolov8s.pt')
model.train(data='data.yaml', epochs=25)
  3. Validation: 
    model.val(data='data.yaml')
  4. Prediction: 
    model.predict(source='path/to/test/images')

Important Notes

  • Segmentation and Classification: Commands are similar, change task and model accordingly
  • Issues & Community Support: YOLO V8 is under heavy development; report issues on the Ultra Analytics GitHub repository
  • Resource Files: Keep data.yaml and annotations in proper structure
  • Output Directory: Model saves results in a designated runs folder

Observations

  • Predicted outputs include detected objects with class names and counts
  • Problem Noted: Predictions are not saved in the designated 'runs/predict' folder currently (under development)

Conclusion

  • Comprehensive introduction and guide to installing, training, validating, and using YOLO V8
  • Encourages community involvement and feedback
  • Promises future tutorial videos covering image segmentation and classification

This lecture provided a solid understanding of YOLO V8’s capabilities and practical steps for utilizing it in custom object detection tasks.