# micromind checkpoints for CIFAR-100

This repository contains checkpoints for the CIFAR-100 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)`   |     75.56%         |      93.5%       |
| `PhiNet(alpha=0.75, beta=1, t_zero=6, num_layers=5, resolution=160)`   |     60.87%         |      86.98%       |
| `PhiNet(alpha=0.35, beta=1, t_zero=6, num_layers=7, resolution=160)`   |     59.77%         |      85.98%       |
| `PhiNet(alpha=0.25, beta=1, t_zero=6, num_layers=7, resolution=160)`   |     54.16%         |      82.32%       |
| `PhiNet(alpha=0.25, beta=1, t_zero=5, num_layers=7, resolution=160)`   |     52.41%         |      81.24%       |

To download and use this repo:

```
from micromind import PhiNet
model = PhiNet.from_pretrained("CIFAR-100", alpha=3.0, beta=0.75, t_zero=6, num_layers=7, num_classes=100, resolution=160)
```

## Authors

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

---
license: mit
---
{"language": ["en"], "license": "mit", "datasets": ["cifar100"], "pipeline_tag": "image-classification"}
image-classification en dataset:cifar100 license:mit region:us
TAGS
#image-classification #en #dataset-cifar100 #license-mit #region-us 

micromind checkpoints for CIFAR-100
===================================


This repository contains checkpoints for the CIFAR-100 dataset for the following networks:


Model: 'PhiNet(alpha=3, beta=0.75, t\_zero=6, num\_layers=7, resolution=160)', Top 1 Accuracy: 75.56%, Top 5 Accuracy: 93.5%
Model: 'PhiNet(alpha=0.75, beta=1, t\_zero=6, num\_layers=5, resolution=160)', Top 1 Accuracy: 60.87%, Top 5 Accuracy: 86.98%
Model: 'PhiNet(alpha=0.35, beta=1, t\_zero=6, num\_layers=7, resolution=160)', Top 1 Accuracy: 59.77%, Top 5 Accuracy: 85.98%
Model: 'PhiNet(alpha=0.25, beta=1, t\_zero=6, num\_layers=7, resolution=160)', Top 1 Accuracy: 54.16%, Top 5 Accuracy: 82.32%
Model: 'PhiNet(alpha=0.25, beta=1, t\_zero=5, num\_layers=7, resolution=160)', Top 1 Accuracy: 52.41%, Top 5 Accuracy: 81.24%


To download and use this repo:


Authors
-------


* @fpaissan
* @matteobeltrami




---


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



TAGS
#image-classification #en #dataset-cifar100 #license-mit #region-us 

TAGS
#image-classification #en #dataset-cifar100 #license-mit #region-us 
