Model Card for NNN (Not a Neural Network)

<!-- Provide a quick summary of what the model is/does. -->

This modelcard aims to be a base template for new models. It has been generated using this raw template. Just a simple exercise I did to learn how to use the PyTorch and TorchHD libraries

Model Details

Model Description

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This MNIST model was made using 2 libraries: PyTorch and TorchHD. The HD in TorchHD stands for Hyperdimensional Computing, which means TorchHD is a library that allows you to do hyperdimensional computing in PyTorch. Hyperdimensional Computing (Or HDC) models are much less accurate than neural networks, that's why this model's accuracy is ~82%

Model Sources [optional]

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Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> This model is intended to be used as an experiment to compare TorchHD models to PyTorch models.

Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> This model is intended to be used for recognizing digits. Please be aware that it has a lower accuracy than a normal PyTorch model.

Downstream Use

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This model could be fine-tuned to improve its accuracy, as it is surprisingly low.

Out-of-Scope Use

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Please do not misuse the model. This model will not work for tasks other than handwritten digit recognition.

Bias, Risks, and Limitations

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This model is too simple and inaccurate to be biased against a social group. The technical limitations are its inaccuracy.

Recommendations

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Users (both direct and downstream) should be aware of the risks, biases and limitations of the model. Be aware of how inaccurate this model is!!!

How to Get Started with the Model

Download both the model and the encoder. Make sure to download their weights too if you want to fine-tune them! After that you can load them in PyTorch.

import torch

# Load the base model and weights
model = torch.load("mnist.pt")
model.load_state_dict(torch.load("mnist_weights.pt"))

# Load the encoder and its weights
encoder = torch.load("mnist_encoder.pt")
encoder.load_state_dict("mnist_encoder_weights.pt")

# Load an image of a handwritten digit.
# sample_image = (load your image here)

# Encode the loaded image
encoded_image = encode(sample_image)
outputs = model(encoded_image)
print(outputs)

Training Details

Training Data

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[Link to MNIST will be added soon]

Training Procedure

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Preprocessing

[More Information Needed]

Training Hyperparameters

Speeds, Sizes, Times

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The training of this model took 1 hour, because I have a potato PC

Evaluation

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Testing Data, Factors & Metrics

Testing Data

<!-- This should link to a Data Card if possible. -->

[Link to MNIST will be added soon]

Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[Accuracy: 82.850%]

Results

[Low accuracy]

Summary

This model is simply too inaccurate for its own good. However, I (Comrade Cat), will try to retrain the model until it has better accuracy.

Model Card Contact

[More Information Needed]