# micromind checkpoints for CIFAR-10

This repository contains checkpoints for the CIFAR-10 dataset for the following networks:

| Model         | Top 1 Accuracy  | Top 5 Accuracy |
| ------------------ |---------------- | -------------- |
| `PhiNet(alpha=3, beta=0.75, t_zero=6, num_layers=7, resolution=160)`   |     93.61%         |      99.77%       |
| `PhiNet(alpha=0.75, beta=1, t_zero=6, num_layers=5, resolution=160)`   |     86.8%         |      99.5%       |
| `PhiNet(alpha=0.35, beta=1, t_zero=6, num_layers=7, resolution=160)`   |     88.08%         |      99.48%       |
| `PhiNet(alpha=0.25, beta=1, t_zero=6, num_layers=7, resolution=160)`   |     84.97%         |      99.3%       |
| `PhiNet(alpha=0.25, beta=1, t_zero=5, num_layers=7, resolution=160)`   |     83.01%         |      99.2%       |

To download and use this repo:

```
from micromind import PhiNet
model = PhiNet.from_pretrained("CIFAR-10", alpha=3.0, beta=0.75, t_zero=6, num_layers=7, num_classes=10, resolution=160)
```

## Authors

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

---
license: mit
---
{"language": ["en"], "license": "mit", "datasets": ["cifar10"], "pipeline_tag": "image-classification"}
image-classification en dataset:cifar10 license:mit region:us
TAGS
#image-classification #en #dataset-cifar10 #license-mit #region-us 

micromind checkpoints for CIFAR-10
==================================


This repository contains checkpoints for the CIFAR-10 dataset for the following networks:


Model: 'PhiNet(alpha=3, beta=0.75, t\_zero=6, num\_layers=7, resolution=160)', Top 1 Accuracy: 93.61%, Top 5 Accuracy: 99.77%
Model: 'PhiNet(alpha=0.75, beta=1, t\_zero=6, num\_layers=5, resolution=160)', Top 1 Accuracy: 86.8%, Top 5 Accuracy: 99.5%
Model: 'PhiNet(alpha=0.35, beta=1, t\_zero=6, num\_layers=7, resolution=160)', Top 1 Accuracy: 88.08%, Top 5 Accuracy: 99.48%
Model: 'PhiNet(alpha=0.25, beta=1, t\_zero=6, num\_layers=7, resolution=160)', Top 1 Accuracy: 84.97%, Top 5 Accuracy: 99.3%
Model: 'PhiNet(alpha=0.25, beta=1, t\_zero=5, num\_layers=7, resolution=160)', Top 1 Accuracy: 83.01%, Top 5 Accuracy: 99.2%


To download and use this repo:


Authors
-------


* @fpaissan
* @matteobeltrami




---


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



TAGS
#image-classification #en #dataset-cifar10 #license-mit #region-us 

TAGS
#image-classification #en #dataset-cifar10 #license-mit #region-us 
