Instructions to use timm/vit_pe_spatial_small_patch16_512.fb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_pe_spatial_small_patch16_512.fb with timm:
import timm model = timm.create_model("hf_hub:timm/vit_pe_spatial_small_patch16_512.fb", pretrained=True) - Transformers
How to use timm/vit_pe_spatial_small_patch16_512.fb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_pe_spatial_small_patch16_512.fb")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_pe_spatial_small_patch16_512.fb", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architecture": "vit_pe_spatial_small_patch16_512", | |
| "num_classes": 0, | |
| "num_features": 384, | |
| "global_pool": "avg", | |
| "pretrained_cfg": { | |
| "tag": "fb", | |
| "custom_load": false, | |
| "input_size": [ | |
| 3, | |
| 512, | |
| 512 | |
| ], | |
| "fixed_input_size": true, | |
| "interpolation": "bicubic", | |
| "crop_pct": 1.0, | |
| "crop_mode": "center", | |
| "mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "num_classes": 0, | |
| "pool_size": null, | |
| "first_conv": "patch_embed.proj", | |
| "classifier": "head", | |
| "license": "custom" | |
| } | |
| } |