An Effective Two-stage Training Paradigm Detector for Small Dataset

• URL: http://arxiv.org/abs/2309.05652v1
• Date: Mon, 11 Sep 2023 17:43:11 GMT
• Title: An Effective Two-stage Training Paradigm Detector for Small Dataset
• Authors: Zheng Wang, Dong Xie, Hanzhi Wang, Jiang Tian
• Abstract summary: The backbone of YOLOv8 is pre-trained as the encoder using the masked image modeling technique. During the test stage, test-time augmentation (TTA) is used to enhance each model, and weighted box fusion (WBF) is implemented to further boost the performance. With the well-designed structure, our approach has achieved 30.4% average precision from 0.50 to 0.95 on the DelftBikes test set, ranking 4th on the leaderboard.
• Score: 13.227589864946477
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning from the limited amount of labeled data to the pre-train model has always been viewed as a challenging task. In this report, an effective and robust solution, the two-stage training paradigm YOLOv8 detector (TP-YOLOv8), is designed for the object detection track in VIPriors Challenge 2023. First, the backbone of YOLOv8 is pre-trained as the encoder using the masked image modeling technique. Then the detector is fine-tuned with elaborate augmentations. During the test stage, test-time augmentation (TTA) is used to enhance each model, and weighted box fusion (WBF) is implemented to further boost the performance. With the well-designed structure, our approach has achieved 30.4% average precision from 0.50 to 0.95 on the DelftBikes test set, ranking 4th on the leaderboard.

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