Instructions to use edadaltocg/resnet18_cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use edadaltocg/resnet18_cifar10 with timm:
import timm model = timm.create_model("hf_hub:edadaltocg/resnet18_cifar10", pretrained=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "architecture": "resnet18", | |
| "num_classes": 10, | |
| "num_features": 512, | |
| "pretrained_cfg": { | |
| "num_classes": 10, | |
| "input_size": [ | |
| 3, | |
| 32, | |
| 32 | |
| ], | |
| "pool_size": [ | |
| 4, | |
| 4 | |
| ], | |
| "crop_pct": 1, | |
| "interpolation": "bilinear", | |
| "fixed_input_size": false, | |
| "mean": [ | |
| 0.4914, | |
| 0.4822, | |
| 0.4465 | |
| ], | |
| "std": [ | |
| 0.2023, | |
| 0.1994, | |
| 0.201 | |
| ], | |
| "first_conv": "conv1", | |
| "classifier": "fc" | |
| }, | |
| "url": "https://huggingface.co/edadaltocg/resnet18_cifar10/resolve/main/pytorch_model.bin", | |
| "input_size": [ | |
| 3, | |
| 32, | |
| 32 | |
| ], | |
| "pool_size": [ | |
| 4, | |
| 4 | |
| ], | |
| "crop_pct": 1, | |
| "interpolation": "bilinear", | |
| "fixed_input_size": false, | |
| "mean": [ | |
| 0.4914, | |
| 0.4822, | |
| 0.4465 | |
| ], | |
| "std": [ | |
| 0.2023, | |
| 0.1994, | |
| 0.201 | |
| ], | |
| "first_conv": "conv1", | |
| "classifier": "fc" | |
| } |