---
license: apache-2.0
datasets:
- mnist
metrics:
- accuracy
pipeline_tag: image-classification

model-index:
- name: mnist_nnn_vision
  results:
  - task:
      type: image-classification             # Required. Example: automatic-speech-recognition
      name: Image Classification            # Optional. Example: Speech Recognition
    dataset:
      type: mnist          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
      name: MNIST         # Required. A pretty name for the dataset. Example: Common Voice (French)
      split: test        # Optional. Example: test
    metrics:
      - type: accuracy         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.9311       # Required. Example: 20.90
        name: Accuracy        # Optional. Example: Test WER
        verified: true              # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
---

# 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](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
Just a simple exercise I did to learn how to use the PyTorch and TorchHD libraries
## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

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]

<!-- Provide the basic links for the model. -->

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

## 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

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

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

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

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

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

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

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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.

```python
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

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[Link to MNIST will be added soon]

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [I don't know yet] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
- **DIMENSIONS:** 11000
- **IMAGE SIZE:** 28
- **NUMBER OF LEVELS:** = 1000
- **BATCH SIZE:** 2
#### Speeds, Sizes, Times

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

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

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### 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]