Description
Demonstrate image classification using a spiking neural network
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Objective – Show as high a test accuracy as possible
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Dataset – EMNIST
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This dataset is an extended version of MNIST that contains handwritten letters in addition to digits. There are 6 splits possible for this dataset. Use the ‘balanced’
split.
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More info at http://pytorch.org/vision/main/generated/torchvision.datasets.EMNIST.html
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Guidelines
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Project to be done in teams of two
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Create a separate function for performing inference on test dataset. You may be asked to demonstrate during viva.
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The learning will be done using ‘Backprop through Time using Surrogate Gradients’ algorithms.
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Since test dataset will be used to check final test accuracy, it cannot be used for training. You may do a train: validation split of your original training dataset.
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Show training loss vs epoch and accuracy vs epoch graphs
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You are free to choose the model, encoding method, loss functions and surrogate functions to meet the objective
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You are expected to utilize a gpu as the runs would take long
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Recommended way is to use the snnTorch library. There are many tutorials available for reference on training SNNs.
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Look at https://snntorch.readthedocs.io/en/latest/tutorials/index.html
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Grading Scheme
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Score |
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Total |
30 |
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Code |
12 |
Implementation of dataset prep, model, optimization |
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Viva |
8 |
and final test inference |
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Communicate understanding of problem statement and |
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Performance |
4 + 6 |
explain approach |
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– 4 marks awarded if test accuracy > 60% ( i.e. |
model is better than coin toss)
– Groups will be ranked according to test accuracy
and awarded remaining 6 marks relatively. Top
group gets full 6 marks
