This paper investigates how adjustments to deep learning architectures impact model performance in image classification. Small-scale experiments generate initial insights although the trends observed are not consistent with the entire dataset. Filtering operations in the image processing pipeline are crucial, with image filtering before pre-processing yielding better results. The choice and order of layers as well as filter placement significantly impact model performance. This study provides valuable insights into optimizing deep learning models, with potential avenues for future research including collaborative platforms.

翻译：本文研究了深度学习架构调整对图像分类模型性能的影响。小规模实验提供了初步见解，但所观察到的趋势与整个数据集并不完全一致。图像处理流程中的滤波操作至关重要，预处理器之前的图像滤波能够带来更好的结果。层的选择与顺序以及滤波器放置位置显著影响模型性能。本研究为优化深度学习模型提供了宝贵见解，未来研究可包括协作平台等潜在方向。