Instructions to use rwightman/test_vit_b16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use rwightman/test_vit_b16 with timm:
import timm model = timm.create_model("hf_hub:rwightman/test_vit_b16", pretrained=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "architecture": "vit_base_patch16_clip_224", | |
| "num_classes": 1000, | |
| "num_features": 768, | |
| "global_pool": "avg", | |
| "model_args": { | |
| "global_pool": "avg", | |
| "act_layer": "silu", | |
| "fc_norm": false | |
| }, | |
| "pretrained_cfg": { | |
| "tag": "laion2b_ft_in12k_in1k", | |
| "custom_load": false, | |
| "input_size": [ | |
| 3, | |
| 224, | |
| 224 | |
| ], | |
| "fixed_input_size": true, | |
| "interpolation": "bicubic", | |
| "crop_pct": 0.95, | |
| "crop_mode": "center", | |
| "mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "num_classes": 1000, | |
| "pool_size": null, | |
| "first_conv": "patch_embed.proj", | |
| "classifier": "head" | |
| } | |
| } |