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Knowledge Base
AUA  ·  Smart Droplets Knowledge Base

Object Detection (Faster R-CNN and YOLO)

Lecturer: Borja Espejo-Garcia

Duration: 2 hours

Participants: 10 people

This workshop introduces key object detection models used in agricultural applications, focusing on Faster R-CNN and YOLO. Participants explore the differences between two-stage and single-stage detectors, comparing trade-offs in accuracy and speed.
The session includes practical labs on fine-tuning a YOLO model for weed detection, data augmentation techniques, and building core object detection components from scratch. Attendees gain hands-on experience with dataset loading, bounding box annotation, training configuration, and performance optimisation for real-time deployment on edge devices.

Presentation:
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Code:
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Data:
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Transfer Learning for small datasets in Weed Identification
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Project Coordination

Dr. Spyros Fountas

Associate Professor
  • Agricultural University of Athens
  • 75 Iera Odos Str. 11855, Athens, Greece
Project Communication

Grigoris Chatzikostas

RFF Partner
  • reframe.food
  • 20 Leontos Sofou str, 57001, Thermi Thessalonikis, Greece

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

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