Francisco Javier Ortega Irizo
Papers
1
Total Citations
10
H-Index
1
About
Francisco Javier Ortega Irizo is a researcher whose work bridges the critical gap between artificial intelligence and real-world efficiency, with a particular focus on deep learning and object detection. His most cited paper, "How efficient deep-learning object detectors are?" (2019, 10 citations), stands as a foundational inquiry into the performance trade-offs of modern computer vision systems, offering key insights into the balance between accuracy and computational cost. This work contributes to the broader field of efficient AI, addressing the pressing need for models that are both powerful and practical for deployment in resource-constrained environments. Ortega Irizo’s research is notable for its emphasis on empirical evaluation and optimization, helping to guide the development of more sustainable and accessible deep learning technologies. His contributions are especially valuable for students and researchers seeking to understand the real-world applicability of object detection algorithms, and his work continues to influence discussions on model efficiency in the era of large-scale AI.
Research Focus
Key Achievements
Top Papers
- 1How efficient deep-learning object detectors are?10 citations · 2019