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In-Situ Fine-Tuning of Wildlife Models in IoT-Enabled Camera Traps for Efficient Adaptation

Created by
  • Haebom

Author

Mohammad Mehdi Rastikerdar, Jin Huang, Hui Guan, Deepak Ganesan

Outline

This paper presents WildFit, a novel adaptation framework, to address the issue of poor accuracy of deep learning models in resource-constrained IoT devices, using a wildlife camera trap as an example. WildFit generates training data through on-device synthesis, focusing on background variations, and uses a drift-aware fine-tuning technique to update the model only when necessary. This maintains accurate species classification even under limited connectivity and energy constraints. Background-aware synthesis is more efficient than existing methods, and drift-aware fine-tuning improves accuracy while reducing the number of updates. As a result, WildFit outperforms existing domain adaptation methods by 20-35% and consumes only 11.2 Wh of energy over 37 days.

Takeaways, Limitations

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Takeaways:
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Presenting an effective solution to the problem of poor performance of deep learning models in resource-constrained IoT environments.
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Presenting an efficient data augmentation and model update strategy using background information.
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Presenting the possibility of implementing sustainable IoT systems through energy-efficient on-device learning.
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It suggests applicability not only to wildlife monitoring but also to various IoT applications.
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Limitations:
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WildFit's performance is highly dependent on background variation, and performance gains may be limited when background variation is small.
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Because it was only evaluated for specific wildlife species and environments, further research is needed to determine generalizability.
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Lack of detailed analysis of the computational and memory requirements for on-device learning.
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Further verification of practical applicability is needed due to the lack of experimental results in various devices and environments.
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