metadata

license: apache-2.0
datasets:
  - mnist
metrics:
  - accuracy
pipeline_tag: image-classification
model-index:
  - name: mnist_nnn_vision
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          type: mnist
          name: MNIST
          split: test
        metrics:
          - type: accuracy
            value: 0.9311
            name: Accuracy
            verified: true

Model Card for NNN (Not a Neural Network)

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

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%

Developed by: Comrade Cat (me)
Shared by: Comrade Cat (me)
Model type: Image Classification
Language(s) (NLP): None
License: Apache 2.0
Finetuned from model: None. This is a pretrained model.

Model Sources [optional]

Repository: Here
Paper: None
Demo: Not available yet.

Uses

This model is intended to be used as an experiment to compare TorchHD models to PyTorch models.

Direct Use

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

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

Out-of-Scope Use

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

Bias, Risks, and Limitations

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

Recommendations

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

[Link to MNIST will be added soon]

Training Procedure

Preprocessing

[More Information Needed]

Training Hyperparameters

Training regime: [I don't know yet]
DIMENSIONS: 11000
IMAGE SIZE: 28
NUMBER OF LEVELS: = 1000
BATCH SIZE: 2

Speeds, Sizes, Times

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

Evaluation

Testing Data, Factors & Metrics

Testing Data

[Link to MNIST will be added soon]

Factors

[More Information Needed]

Metrics

[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]