Efficient Transferability Assessment for Selection of Pre-trained Detectors
<|reference_start|>Efficient Transferability Assessment for Selection of Pre-trained Detectors: Large-scale pre-training followed by downstream fine-tuning is an effective solution for transferring deep-learning-based models. Since finetuning all possible pre-trained models is computational costly, we aim to predict the transferability performance of these pre-trained models in a computational efficient manner. Different from previous work that seek out suitable models for downstream classification and segmentation tasks, this paper studies the efficient transferability assessment of pre-trained object detectors. To this end, we build up a detector transferability benchmark which contains a large and diverse zoo of pre-trained detectors with various architectures, source datasets and training schemes. Given this zoo, we adopt 7 target datasets from 5 diverse domains as the downstream target tasks for evaluation. Further, we propose to assess classification and regression sub-tasks simultaneously in a unified framework. Additionally, we design a complementary metric for evaluating tasks with varying objects. Experimental results demonstrate that our method outperforms other state-of-the-art approaches in assessing transferability under different target domains while efficiently reducing wall-clock time 32$\times$ and requires a mere 5.2\% memory footprint compared to brute-force fine-tuning of all pre-trained detectors.<|reference_end|>
@article{wang2024efficient,
    title={Efficient Transferability Assessment for Selection of Pre-trained
  Detectors},
    author={Zhao Wang, Aoxue Li, Zhenguo Li, Qi Dou},
    journal={arXiv preprint arXiv:2403.09432},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09432},
    primaryClass={cs.CV}
}