Instructions to use timm/vit_giantopt_patch16_siglip_gap_256.v2_webli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_giantopt_patch16_siglip_gap_256.v2_webli with timm:
import timm model = timm.create_model("hf-hub:timm/vit_giantopt_patch16_siglip_gap_256.v2_webli", pretrained=True) - Transformers
How to use timm/vit_giantopt_patch16_siglip_gap_256.v2_webli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_giantopt_patch16_siglip_gap_256.v2_webli")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_giantopt_patch16_siglip_gap_256.v2_webli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from timm/vit_giantopt_patch16_siglip_gap_256.v2_webli: direct link, hf CLI and curl.
- Browser
- Download file 583 Bytes
-
https://huggingface.co/timm/vit_giantopt_patch16_siglip_gap_256.v2_webli/resolve/main/config.json
- Command line
-
hf download hf://timm/vit_giantopt_patch16_siglip_gap_256.v2_webli/config.json
-
curl -L -o config.json https://huggingface.co/timm/vit_giantopt_patch16_siglip_gap_256.v2_webli/resolve/main/config.json
583 Bytes
| { | |
| "architecture": "vit_giantopt_patch16_siglip_gap_256", | |
| "num_classes": 0, | |
| "num_features": 1536, | |
| "global_pool": "avg", | |
| "pretrained_cfg": { | |
| "tag": "v2_webli", | |
| "custom_load": false, | |
| "input_size": [ | |
| 3, | |
| 256, | |
| 256 | |
| ], | |
| "fixed_input_size": true, | |
| "interpolation": "bicubic", | |
| "crop_pct": 0.9, | |
| "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" | |
| } | |
| } |