# micromind checkpoints for MNIST

This repository contains checkpoints for the MNIST dataset for the following networks:

| Model         | Top 1 Accuracy  | Top 5 Accuracy |
| ------------------ |---------------- | -------------- |
| `PhiNet(alpha=0.5, beta=1, t_zero=6, num_layers=4, resolution=28)`   |     98.96%         |      100%       |
| `PhiNet(alpha=0.75, beta=1, t_zero=6, num_layers=5, resolution=28)`   |     99.03%         |      99.98%       |
| `PhiNet(alpha=0.35, beta=1, t_zero=6, num_layers=7, resolution=28)`   |     98.72%         |      99.99%       |
| `PhiNet(alpha=0.25, beta=1, t_zero=6, num_layers=7, resolution=28)`   |     98.84%         |      99.99%       |
| `PhiNet(alpha=0.25, beta=1, t_zero=5, num_layers=7, resolution=28)`   |     98.76%         |      99.97%       |

To download and use this repo:

```
from micromind import PhiNet
model = PhiNet.from_pretrained("MNIST", alpha=0.5, beta=1.0, t_zero=6, num_layers=4, num_classes=10, resolution=28)
```

## Authors

- [@fpaissan](https://www.github.com/fpaissan)
- [@matteobeltrami](https://www.github.com/matteobeltrami)

---
license: mit
---
{"language": ["en"], "license": "mit", "datasets": ["mnist"], "pipeline_tag": "image-classification"}
image-classification en dataset:mnist license:mit region:us
TAGS
#image-classification #en #dataset-mnist #license-mit #region-us 

micromind checkpoints for MNIST
===============================


This repository contains checkpoints for the MNIST dataset for the following networks:


Model: 'PhiNet(alpha=0.5, beta=1, t\_zero=6, num\_layers=4, resolution=28)', Top 1 Accuracy: 98.96%, Top 5 Accuracy: 100%
Model: 'PhiNet(alpha=0.75, beta=1, t\_zero=6, num\_layers=5, resolution=28)', Top 1 Accuracy: 99.03%, Top 5 Accuracy: 99.98%
Model: 'PhiNet(alpha=0.35, beta=1, t\_zero=6, num\_layers=7, resolution=28)', Top 1 Accuracy: 98.72%, Top 5 Accuracy: 99.99%
Model: 'PhiNet(alpha=0.25, beta=1, t\_zero=6, num\_layers=7, resolution=28)', Top 1 Accuracy: 98.84%, Top 5 Accuracy: 99.99%
Model: 'PhiNet(alpha=0.25, beta=1, t\_zero=5, num\_layers=7, resolution=28)', Top 1 Accuracy: 98.76%, Top 5 Accuracy: 99.97%


To download and use this repo:


Authors
-------


* @fpaissan
* @matteobeltrami




---


license: mit
------------



TAGS
#image-classification #en #dataset-mnist #license-mit #region-us 

TAGS
#image-classification #en #dataset-mnist #license-mit #region-us 
