Summary of Evaluating the Energy Efficiency Of Few-shot Learning For Object Detection in Industrial Settings, by Georgios Tsoumplekas et al.
Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings
by Georgios Tsoumplekas, Vladislav Li, Ilias Siniosoglou, Vasileios Argyriou, Sotirios K. Goudos, Ioannis D. Moscholios, Panagiotis Radoglou-Grammatikis, Panagiotis Sarigiannidis
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning model’s performance has driven innovation in Artificial Intelligence (AI), leading to larger and more complex models. However, sustainability and energy efficiency are critical when deploying these models in industrial settings. To alleviate the burden of lengthy training times and minimize energy consumption, a finetuning approach is examined to adapt standard object detection models to downstream tasks. The paper presents a case study and evaluation of the energy demands of developed models applied to object detection benchmark datasets from volatile industrial environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a way to make AI models more efficient while keeping them effective. They fine-tune existing object detection models for specific tasks, reducing the need for long training times and large amounts of data. The study evaluates the energy usage of these adapted models on various benchmark datasets from real-world industrial settings. By finding the right balance between performance and efficiency, this research aims to make AI more practical and sustainable. |
Keywords
* Artificial intelligence * Machine learning * Object detection
