  The Vision Challenge Track 1 for Data-Effificient Defect Detection requires
competitors to instance segment 14 industrial inspection datasets in a
data-defificient setting. This report introduces the technical details of the
team Aoi-overfifitting-Team for this challenge. Our method focuses on the key
problem of segmentation quality of defect masks in scenarios with limited
training samples. Based on the Hybrid Task Cascade (HTC) instance segmentation
algorithm, we connect the transformer backbone (Swin-B) through composite
connections inspired by CBNetv2 to enhance the baseline results. Additionally,
we propose two model ensemble methods to further enhance the segmentation
effect: one incorporates semantic segmentation into instance segmentation,
while the other employs multi-instance segmentation fusion algorithms. Finally,
using multi-scale training and test-time augmentation (TTA), we achieve an
average mAP@0.50:0.95 of more than 48.49% and an average mAR@0.50:0.95 of
66.71% on the test set of the Data Effificient Defect Detection Challenge. The
code is available at https://github.com/love6tao/Aoi-overfitting-team
