Instructions to use nateraw/some-timm-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use nateraw/some-timm-model with timm:
import timm model = timm.create_model("hf_hub:nateraw/some-timm-model", pretrained=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "num_classes": 37, | |
| "input_size": [ | |
| 3, | |
| 224, | |
| 224 | |
| ], | |
| "pool_size": [ | |
| 7, | |
| 7 | |
| ], | |
| "crop_pct": 0.95, | |
| "interpolation": "bicubic", | |
| "mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "first_conv": "conv1", | |
| "classifier": "fc", | |
| "url": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1_0-14fe96d1.pth", | |
| "architecture": "resnet50", | |
| "num_features": 2048, | |
| "labels": [ | |
| "LABEL_0", | |
| "LABEL_1", | |
| "LABEL_2", | |
| "LABEL_3", | |
| "LABEL_4", | |
| "LABEL_5", | |
| "LABEL_6", | |
| "LABEL_7", | |
| "LABEL_8", | |
| "LABEL_9", | |
| "LABEL_10", | |
| "LABEL_11", | |
| "LABEL_12", | |
| "LABEL_13", | |
| "LABEL_14", | |
| "LABEL_15", | |
| "LABEL_16", | |
| "LABEL_17", | |
| "LABEL_18", | |
| "LABEL_19", | |
| "LABEL_20", | |
| "LABEL_21", | |
| "LABEL_22", | |
| "LABEL_23", | |
| "LABEL_24", | |
| "LABEL_25", | |
| "LABEL_26", | |
| "LABEL_27", | |
| "LABEL_28", | |
| "LABEL_29", | |
| "LABEL_30", | |
| "LABEL_31", | |
| "LABEL_32", | |
| "LABEL_33", | |
| "LABEL_34", | |
| "LABEL_35", | |
| "LABEL_36" | |
| ], | |
| "hf_hub": "nateraw/resnet50-oxford-iiit-pet" | |
| } |