YOLO for Segmentation

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prerequisites
Intermediate Python • Basics of YOLO and object detection • Familiarity with training deep learning models
skills learned
Setting up YOLOv8 for segmentation tasks • Training and optimizing detection models • Visualizing segmentation outputs
Satyajit Pattnaik
1 week · 4-6 hours per week · INTERMEDIATE
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Look inside

In this liveProject, you’ll team up with WildVisionTech, a startup focused on wildlife conservation and monitoring. WildVisionTech needs a system that can measure and monitor aspects of elephants who are photographed in their camera traps, including tusk length, size, and skin texture. To assist them, you’ll build a deep learning-based segmentation system using the powerful YOLOv8 segmentation model. Start by preparing a YOLO-compatible dataset with annotated images, label files, and a data.yaml configuration, ensuring proper structure and verification. Next, install and configure Ultralytics YOLOv8 to support segmentation tasks on your dataset. Train the model while monitoring validation performance and saving the best weights, then test it on unseen samples to evaluate generalization. Finally, visualize segmentation results by comparing the model’s predictions with ground truth annotations.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Satyajit Pattnaik
Dr. Satyajit Pattnaik is a Lead Data/AI Architect with over 14 years of experience in software development, focusing on AI, data migration, and cloud computing. He has held key roles at PALO IT and Xccelerate, leading digital transformations and developing innovative Data and AI solutions. Dr.Pattnaik has served as a keynote speaker at international conferences and contributed to several publications and online courses in data analytics, data science and AI. He holds a Doctorate in Business Analytics and an MSc in Data Science, demonstrating his commitment to advancing the field.

prerequisites

The liveProject is for aspiring machine learning engineers, AI-curious developers, and students looking to dive deep into deep learning through a fun, real-world project.


TOOLS
  • Beginner Python
  • Beginner TensorFlow and Keras
  • Beginner Jupyter Notebook
TECHNIQUES
  • Basics of deep learning and neural networks
  • Basics of image processing
  • Basics of deep learning model evaluation

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