After logging in on your machine, you can download the checkpoints:

```
from huggingface_hub import hf_hub_download

REPO_ID = "micromind/ImageNet"
FILENAME = "v5/state_dict.pth.tar"

model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
```

followed by:
```
model = PhiNet(
    input_shape=(3, 224, 224),
    alpha=...,
    num_layers=...,
    beta=...,
    t_zero=...,
    include_top=True,
    num_classes=1000,
    compatibility=False,
    divisor=8,
    downsampling_layers=[4,5,7]
)

model.load_state_dict(torch.load(model_path))
```
*Note* for v1, when initializing the network, use:
```
downsampling_layers=[5,7]
```

Performance:
| Model name | Acc@1 | Acc@5 |
|------------|-------|-------|
| v1         |   71.18%    |   89.65%    |
| v2         |   65.21%    |   85.82%    |
| v3         |   64.69%    |   86.15%    |
| v5         |   67.99%    |   87.53%    |
| v6         |   61.86%    |   83.44%    |
| v7         |   53.66%    |   77.13%    |