Duration: 2 hours Participants: 30 people This seminar explores advanced techniques that enhance computer vision performance in agricultural settings. Starting with a quick review of CNNs, evaluation metrics, and image representations, it introduces state-of-the-art methods such as transformer backbones, multi-scale […]
Duration: 1 hours Participants: 10 people This seminar introduces the principles and tools behind effective image annotation for object detection. It covers best practices for defining classes and regions (bounding boxes, polygons, masks), avoiding data leakage, and ensuring annotation quality […]
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 […]
Lecturer: Borja Espejo-Garcia Duration: 2 hours Participants: 10 people This seminar explores how transfer learning can accelerate weed identification in agriculture using limited training data. Instead of training models from scratch, participants learn to adapt pre-trained vision models (CNNs and […]
Lecturer: Borja Espejo-Garcia Duration: 2 hours Participants: 15 people In this event, we analyzed the difference between CNNs and transformers by implementing computer vision systems using both technologies. CNNs are particularly suited for processing grid-like data, such as images, where […]
Lecturer: Borja Espejo-Garcia Duration: 2 hours Participants: – The activity directly relates to the goals and objectives of the Smart Droplets project, particularly in the development and implementation of computer vision systems for anomaly detection and optimization of spraying operations. […]
Lecturer: Borja Espejo-Garcia Duration: 2 hours Participants: – The activity aligns closely with the goals and objectives of the Smart Droplets project. By covering the main pillars of Machine Learning, specifically in the context of computer vision methods, the event […]
