VoltaVision: A Transfer Learning model for electronic component classification
This addresses a domain-specific problem for electronic component classification, but it appears incremental as it builds on existing transfer learning methods.
The paper tackles the problem of classifying electronic components by introducing VoltaVision, a lightweight CNN, and finds that transfer learning from a similar task yields better results than state-of-the-art models trained on general datasets, though no concrete numbers are provided.
In this paper, we analyze the effectiveness of transfer learning on classifying electronic components. Transfer learning reuses pre-trained models to save time and resources in building a robust classifier rather than learning from scratch. Our work introduces a lightweight CNN, coined as VoltaVision, and compares its performance against more complex models. We test the hypothesis that transferring knowledge from a similar task to our target domain yields better results than state-of-the-art models trained on general datasets. Our dataset and code for this work are available at https://github.com/AnasIshfaque/VoltaVision.