Instructions to use rhazis/resnet18-random-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use rhazis/resnet18-random-classifier with timm:
import timm model = timm.create_model("hf-hub:rhazis/resnet18-random-classifier", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download config.json from rhazis/resnet18-random-classifier: direct link, hf CLI and curl.
- Browser
- Download file 477 Bytes
-
https://huggingface.co/rhazis/resnet18-random-classifier/resolve/main/config.json
- Command line
-
hf download hf://rhazis/resnet18-random-classifier/config.json
-
curl -L -o config.json https://huggingface.co/rhazis/resnet18-random-classifier/resolve/main/config.json
477 Bytes
| { | |
| "url": "https://download.pytorch.org/models/resnet18-5c106cde.pth", | |
| "num_classes": 4, | |
| "input_size": [ | |
| 3, | |
| 224, | |
| 224 | |
| ], | |
| "pool_size": [ | |
| 7, | |
| 7 | |
| ], | |
| "crop_pct": 0.875, | |
| "interpolation": "bilinear", | |
| "mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "first_conv": "conv1", | |
| "classifier": "fc", | |
| "architecture": "resnet18", | |
| "num_features": 512, | |
| "labels": [ | |
| "a", | |
| "b", | |
| "c", | |
| "d" | |
| ] | |
| } |