Instructions to use timm/vit_base_patch16_siglip_gap_224.webli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_base_patch16_siglip_gap_224.webli with timm:
import timm model = timm.create_model("hf-hub:timm/vit_base_patch16_siglip_gap_224.webli", pretrained=True) - Transformers
How to use timm/vit_base_patch16_siglip_gap_224.webli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_base_patch16_siglip_gap_224.webli")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_base_patch16_siglip_gap_224.webli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add model
Browse files- README.md +10 -0
- config.json +33 -0
- model.safetensors +3 -0
- pytorch_model.bin +3 -0
README.md
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---
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tags:
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- image-feature-extraction
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- timm
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library_name: timm
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license: apache-2.0
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---
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# Model card for vit_base_patch16_siglip_gap_224.webli
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('`timm` SigLIP (image encoder only, with global avg pooling) weights from https://huggingface.co/timm/ViT-B-16-SigLIP',)
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config.json
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{
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"architecture": "vit_base_patch16_siglip_gap_224",
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"num_classes": 0,
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"num_features": 768,
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"global_pool": "avg",
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"pretrained_cfg": {
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"tag": "webli",
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"custom_load": false,
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"input_size": [
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3,
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224,
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224
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],
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"fixed_input_size": true,
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"interpolation": "bicubic",
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"crop_pct": 0.9,
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"crop_mode": "center",
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"mean": [
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0.5,
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0.5,
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0.5
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],
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"std": [
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0.5,
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0.5,
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0.5
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],
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"num_classes": 0,
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"pool_size": null,
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"first_conv": "patch_embed.proj",
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"classifier": "head"
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}
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:a8f0ff132dddb9e19e730bc8af0c7e487f08c52b84b75c7b3dc25cbf7b358af1
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size 343202320
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:2338509effd7c1e50cb5c7150175cd42063abaf1f2ecf87c9c9b2147b1f0ee15
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size 343244510
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