Real-World Object Detection: Waste Sorting & Tomato Ripeness, Use YOLO and computer vision to build object detection systems for smart waste management and tomato ripeness detection..
Course Description
Are you ready to apply computer vision to real-world problems?
In this hands-on course, you’ll build two complete object detection projects: one for identifying household waste items (like plastic, glass, and paper), and another for detecting ripe and unripe tomatoes using the latest YOLOv10 model.
We’ll walk you through each step of the pipeline from dataset preparation and annotation to training and deploying your own AI models. You’ll gain practical experience with tools like Annotate Lab, Gradio, and Ultralytics YOLO, while also learning how data augmentation and evaluation metrics can improve model performance.
Whether you’re interested in sustainability, agriculture, or real-time AI applications, this course provides both the theory and implementation you need to bring AI to life.
By the end of this course, you will:
- Train a YOLOv10 model to detect ripe vs. unripe tomatoes
- Build an object detector for sorting waste categories
- Annotate images using Annotate-Lab with YOLO format
- Apply data augmentation to boost performance
- Deploy your model using Gradio on Hugging Face Spaces
- Export and run your model on mobile devices (optional module)
This course is ideal for:
- Developers and data scientists curious about object detection
- Environmental and agri-tech enthusiasts
- Anyone looking to learn YOLOv10 with practical projects
Enroll today and build AI tools that make an impact from waste bins to tomato fields.
