| 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% | | |