| 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% | |
|
|
