Lightweight Image Classification of Raptor Species for Edge Devices: Rare-Species Dataset Expansion via Video Frame Extraction, Knowledge Distillation, and TensorRT Deployment
Quick Answer
This study presents a lightweight raptor species classification system for edge devices, utilizing DINOv2-L to distill MobileNetV4, ViT-Small, and EfficientNet-B0.
Quick Take
The dataset was expanded to 12,519 images, achieving a macro recall of 0.935 with EfficientNet-B0 deployed on NVIDIA Jetson Orin Nano at 313 images/s, significantly improving misclassification rates for White-tailed Eagles.
Key Points
- Expanded dataset from 463 to 2,050 images for Steller's Sea Eagle.
- Achieved macro recall of 0.935 with a three-student ensemble.
- EfficientNet-B0 deployed at 3.19 ms/image on NVIDIA Jetson Orin Nano.
- Misclassification of White-tailed Eagles reduced from 61% to 15%.
- Dataset expansion and teacher re-fine-tuning were key to performance gains.
Paper Resources
Source Excerpt
We investigate lightweight raptor-species classification for real-time edge deployment in wind-turbine collision mitigation. Using DINOv2-L (304M parameters) as a teacher, we distilled three lightweight students (MobileNetV4, ViT-Small, and EfficientNet-B0). To reduce confusion between closely related species, we expanded the dataset to 12,519 images, including an increase in Steller's Sea Eagle images from 463 to 2,050 via video-frame extraction. Under a group split that separates samples at th
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CV
See more →ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
ProMoE-FL introduces a Prototype-conditioned Mixture-of-Experts framework for multimodal federated learning, effectively addressing missing modalities. It outperforms existing methods on four chest X-ray datasets, demonstrating superior feature synthesis capabilities in both homogeneous and heterogeneous settings.
