对比不同图像特征数据集对自监督模型分类性能的影响
Explorations in Self-Supervised Learning: Dataset Composition Testing for Object Classification
- 用不同光照、分辨率等特征的公寓图像数据预训练SimCLR模型
- 深度图预训练在低分辨率图像上表现更优，RGB图在高分辨率上更强
- 提高训练图像亮度可提升低分辨率图像性能，不影响高分辨率表现
本文研究了基于不同图像特征（模态、亮度、尺寸、视角）的数据集采样与预训练对自监督学习（SSL）模型在物体分类任务中性能的影响。从Omnidata平台选取两类公寓图像数据集，分别基于模态、亮度、图像尺寸和相机视场进行采样，并用于预训练SimCLR模型。将预训练模型生成的编码迁移至监督式ResNet-50模型进行物体分类。通过A/B测试发现，使用深度图预训练的模型在低分辨率图像上表现更佳，而使用RGB图像预训练的模型在高分辨率图像上更具优势。同时发现，提高训练图像的亮度能有效提升模型在低分辨率图像上的性能，且不会对高分辨率图像的表现造成负面影响。
原文摘要 · Abstract (English)
This paper investigates the impact of sampling and pretraining using datasets with different image characteristics on the performance of self-supervised learning (SSL) models for object classification. To do this, we sample two apartment datasets from the Omnidata platform based on modality, luminosity, image size, and camera field of view and use them to pretrain a SimCLR model. The encodings generated from the pretrained model are then transferred to a supervised Resnet-50 model for object classification. Through A/B testing, we find that depth pretrained models are more effective on low resolution images, while RGB pretrained models perform better on higher resolution images. We also discover that increasing the luminosity of training images can improve the performance of models on low resolution images without negatively affecting their performance on higher resolution images.
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