An Effective Two-stage Training Paradigm Detector for Small Dataset
Zheng Wang Dong Xie Hanzhi Wang Jiang Tian
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.