
# 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]
{"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": {"name": "MNIST", "type": "mnist", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.9311, "name": "Accuracy", "verified": true}]}]}]}
pytorch image-classification dataset:mnist license:apache-2.0 model-index region:us
TAGS
#pytorch #image-classification #dataset-mnist #license-apache-2.0 #model-index #region-us 


# 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.



## Training Details

### Training Data



[Link to MNIST will be added soon]

### Training Procedure 



#### Preprocessing




#### 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





#### 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


# 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. ## Training Details ### Training Data



[Link to MNIST will be added soon] ### Training Procedure #### Preprocessing #### 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 #### 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
TAGS
#pytorch #image-classification #dataset-mnist #license-apache-2.0 #model-index #region-us 
 # 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. ## Training Details ### Training Data



[Link to MNIST will be added soon] ### Training Procedure #### Preprocessing #### 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 #### 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
TAGS
#pytorch #image-classification #dataset-mnist #license-apache-2.0 #model-index #region-us 
# 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.## Training Details### Training Data



[Link to MNIST will be added soon]### Training Procedure#### Preprocessing#### 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#### 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