shiny0510 / fashionMNIST_ResNet_master

A ResNet34 implementation of fashionMNIST in pytorch

fashionMNIST_ResNet34

ResNet34 模型

模型结构框图

python相关配置

• python 3.6
• modelsummary==1.1.7
• music21==5.7.2
• numpy==1.16.4
• pandas==1.0.4
• tensorboard==1.14.0
• torch==1.4.0+cu100
• torchvision==0.5.0+cu100

优化函数

	optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay=0.1)
	scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.6)

模型准确率

训练集准确率	验证集准确率	测试集准确率
88.38%	88.57%	90.26%

模型训练

CUDA_VISIBLE_DEVICE=0 python train.py

CUDA_VISIBLE_DEVICE对应显卡序号，本实验所用显卡为一块GeForce GTX 1080 Ti：

nvidia-smi

训练时会自动保存验证集准确率较高的pth文件，以供测试

模型测试

由于本数据集没有区分测试集和验证集，故每次迭代随机从测试集中取batch_size的样本作为验证集，方便训练过程调整超参数

将测试集中的PATH改为对应保存的pth文件即可

CUDA_VISIBLE_DEVICE=0 python test.py

TO DO

• README.md in English

Reference

• [1] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016

• [2] Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. Han Xiao, Kashif Rasul, Roland Vollgraf. arXiv:1708.07747