Deep Neural Network Compression for Lightweight and Accurate Fish Classification
Communications in Computer and Information Science, ISSN: 1865-0937, Vol: 2326 CCIS, Page: 300-318
2025
- 3Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures3
- Readers3
Conference Paper Description
This research demonstrates the effectiveness of compression of deep learning models in a fish species classification system. In a bid to enhance model efficiency, response-based knowledge distillation was employed, balancing model compression and accuracy retention. The underwater footage collected in the Seychelles was annotated in collaboration with the South African Institute for Aquatic Biodiversity (SAIAB), and named the Aldabra data set. The accuracies of the MobileNet and DenseNet models were 89.26% and 86.78%, respectively, in the Aldabra dataset. Although the student model (MobileNet) achieved a lower accuracy, it enabled a three-fold reduction in parameters. In particular, in the much larger Fish4Knowledge dataset, MobileNet trained via knowledge distillation exceeded ResNet’s accuracy, reaching 96.64% with an 18.7-fold size reduction. This performance improvement was attributed to efficient learning facilitated by knowledge distillation. The edge computing-friendly MobileNet model assimilates knowledge from the much larger ResNet output with information gain.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85211779688&origin=inward; http://dx.doi.org/10.1007/978-3-031-78255-8_18; https://link.springer.com/10.1007/978-3-031-78255-8_18; https://dx.doi.org/10.1007/978-3-031-78255-8_18; https://link.springer.com/chapter/10.1007/978-3-031-78255-8_18
Springer Science and Business Media LLC
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