Instructions to use timm/vit_so400m_patch16_siglip_gap_512.v2_webli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_so400m_patch16_siglip_gap_512.v2_webli with timm:
import timm model = timm.create_model("hf-hub:timm/vit_so400m_patch16_siglip_gap_512.v2_webli", pretrained=True) - Transformers
How to use timm/vit_so400m_patch16_siglip_gap_512.v2_webli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_so400m_patch16_siglip_gap_512.v2_webli")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_so400m_patch16_siglip_gap_512.v2_webli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 581 Bytes
f8f3855 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"architecture": "vit_so400m_patch16_siglip_gap_512",
"num_classes": 0,
"num_features": 1152,
"global_pool": "avg",
"pretrained_cfg": {
"tag": "v2_webli",
"custom_load": false,
"input_size": [
3,
512,
512
],
"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"
}
} |