Instructions to use DeKUT-DSAIL/efficientnetv2-xl-cifar10-upsample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use DeKUT-DSAIL/efficientnetv2-xl-cifar10-upsample with timm:
import timm model = timm.create_model("hf_hub:DeKUT-DSAIL/efficientnetv2-xl-cifar10-upsample", pretrained=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "timm_name": "tf_efficientnetv2_xl", | |
| "num_classes": 10, | |
| "input_mode": "upsample", | |
| "image_size": 224, | |
| "mean": [ | |
| 0.4914, | |
| 0.4822, | |
| 0.4465 | |
| ], | |
| "std": [ | |
| 0.247, | |
| 0.2435, | |
| 0.2616 | |
| ], | |
| "classes": [ | |
| "airplane", | |
| "automobile", | |
| "bird", | |
| "cat", | |
| "deer", | |
| "dog", | |
| "frog", | |
| "horse", | |
| "ship", | |
| "truck" | |
| ], | |
| "metrics": { | |
| "top1": 99.07, | |
| "top5": 99.94, | |
| "ece": 0.1157, | |
| "f1_macro": 0.9907, | |
| "auc_macro": 0.9993 | |
| } | |
| } | |