针对定量相位成像技术中样本形态信息提取繁琐不利于自动化检测分析的问题，探索了基于小规模数据集对轮廓相似的相位物体进行精准识别的可行性及其训练策略。分别建立了包括聚苯乙烯微球、红细胞等4类样本的相位分布和干涉条纹数据集。构建了一个卷积神经网络（CNN）模型成功实现对相位图的识别，进而对不同样品相位值进行变换以增大识别难度，并通过改进网络模型在验证集上成功识别出所有样品类型。为简化检测流程，对4类样本对应的干涉条纹进行了识别，用残差模块改善CNN模型的网络退化问题实现了准确分类。针对条纹可见度、载波频率复杂多变的实际情况，分别考查了其对识别准确率的影响。通过优化训练集提高了模型的识别效率，表明了机器学习技术在相位信息识别方面的潜力。

Aiming at the problem that the extraction of sample morphological information in quantitative phase imaging technology is cumbersome and not conducive to automatic detection and analysis, the feasibility and training strategy of an accurate recognition of phase objects with similar contour based on small-scale datasets are explored. The phase distribution and interference fringe datasets of four types of samples, including polystyrene microspheres and red blood cells are established accordingly. A convolution neural network (CNN) model is constructed to recognize the phase diagram successfully, and then the phase values of different samples are transformed to increase recognition difficulty. All sample types are successfully recognized on the verification set by improving the network model. To simplify the detection, the interference fringes corresponding to four types of samples are identified. The residual module is used to improve the network degradation of CNN model and realize an accurate classification. According to the actual situation of complex and changeable fringe visibility and carrier frequency, the impact on the recognition accuracy is investigated, respectively. The recognition efficiency of the model is improved via optimizing the training set, which shows the potential of machine learning technology in phase information recognition.