fashion-mnist-pytorch
A simple CNN to classify fashion mnist dataset. The following network provides 90% test accuracy. On the MNIST dataset, the test accuracy is 99.2%.
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Layer (type) Output Shape Param #
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Conv2d-1 [-1, 32, 26, 26] 832
ReLU-2 [-1, 32, 26, 26] 0
BatchNorm2d-3 [-1, 32, 26, 26] 64
MaxPool2d-4 [-1, 32, 13, 13] 0
Conv2d-5 [-1, 64, 13, 13] 18,496
ReLU-6 [-1, 64, 13, 13] 0
BatchNorm2d-7 [-1, 64, 13, 13] 128
MaxPool2d-8 [-1, 64, 6, 6] 0
Dropout-9 [-1, 64, 6, 6] 0
Linear-10 [-1, 1024] 2,360,320
ReLU-11 [-1, 1024] 0
Linear-12 [-1, 10] 10,250
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Total params: 2,390,090
Trainable params: 2,390,090
Non-trainable params: 0
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Input size (MB): 0.00
Forward/backward pass size (MB): 0.83
Params size (MB): 9.12
Estimated Total Size (MB): 9.96
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Setup
- Clone this repository
git clone https://github.com/gmuraleekrishna/fashion-mnist-pytorch.git - Change directory to
fashion-mnist-pytorch. - Run
pip install requirements.pyto install the required pachages.
Training
Run training by running python main.py --tensorboard. Best weights will be saved in fashion-mnist.pth.
Testing
Run testing by running python main.py --test --file fashion-mnist.pth